CN112368589A - Network architecture and method for location services - Google Patents

Abstract

Translated fromChinese

本发明公开了一种用于确定无线设备在异构无线通信环境中的位置的分离架构。该设备或环境中的另一个部件内的检测器接收包括用于设备的定位信号的参数的信号。该参数描述了信号内的预先已知信号。还接收包括信号的每个帧起始以及协助数据和辅助信息的附加元数据。基于定位信号的参数来检测预先已知信号。然后对从预先已知信号中提取的样本进行处理和压缩，并将该样本与其他收集数据一起发送到远离检测器的定位服务器。该定位服务器使用信息以及关于环境的类似信息来计算设备的位置以及执行设备的跟踪和导航，并且将此类结果报告给环境。

The present invention discloses a separation architecture for determining the location of a wireless device in a heterogeneous wireless communication environment. A detector within the device or another component in the environment receives a signal that includes parameters for the device's location signal. This parameter describes a previously known signal within the signal. Additional metadata including the start of each frame of the signal as well as assistance data and side information is also received. A previously known signal is detected based on the parameters of the positioning signal. A sample taken from a pre-known signal is then processed and compressed, and sent along with other collected data to a positioning server remote from the detector. The location server uses the information and similar information about the environment to calculate the location of the device and to perform tracking and navigation of the device, and reports such results to the environment.

Description

Network architecture and method for location services

Cross reference to related patent applications

This application claims the benefit of provisional U.S. patent application No. 62/653450 filed on 5.4.2018, provisional U.S. patent application No. 62/648883 filed on 27.3.2018, and U.S. patent application No. 16/164724 filed on 18.10.2018.

Technical Field

The present embodiments relate to wireless communication and wireless network systems and systems for Radio Frequency (RF) based identification, object tracking and location, including Real Time Location Services (RTLS), LTE based location services, and location as a service (Laas). The present embodiments also relate to a unified framework/platform (architecture, functional entities and operations) that should accommodate positioning technologies, methods/technologies including all wireless devices, wireless signals, and including hybrid/converged positioning.

Background

RF-based identification and location systems for determining the relative or geographic location of objects are commonly used for tracking single objects or groups of objects and for tracking individuals. Conventional positioning systems have been used for position determination in open outdoor environments. RF-based Global Positioning Systems (GPS)/Global Navigation Satellite Systems (GNSS) and assisted GPS/GNSS are commonly used. However, conventional positioning systems suffer from certain inaccuracies when positioning objects in closed (i.e., indoor) environments as well as outdoor environments.

Cellular wireless communication systems provide various methods of locating the position of User Equipment (UE) indoors as well as in environments where GPS is less suitable. The most accurate method is a positioning technique based on a multi-point positioning/three-point positioning method. For example, release 9 of the Long Term Evolution (LTE) standard specifies the downlink observed time difference of arrival (DL-OTDOA), andrelease 11 specifies the uplink time difference of arrival (U-TDOA) technique as a derivative of the multipoint/three point positioning method.

Since time synchronization errors affect positioning accuracy, a fundamental requirement for multi-point positioning/three-point positioning based systems is a complete and accurate time synchronization of the system with a single common reference time. In cellular networks, the DL-OTDOA and U-TDOA location methods also require that transmissions from multiple antennas be time synchronized in the case of DL-OTDOA, or multiple receivers be time synchronized in the case of U-TDOA.

Release 9 and release 11 of the LTE standard do not specify time synchronization accuracy for positioning purposes, leaving the positioning purpose at the wireless/cellular service provider. On the other hand, these standards do provide limitations on ranging accuracy. For example, when a 10MHz ranging signal bandwidth is used, a DL-OTDOA reliability of 50 meters @ 67% and a U-TDOA reliability of 100 meters @ 67% are required.

The above limitations are caused by a combination of ranging measurement errors and errors caused by a lack of accurate synchronization (e.g., time synchronization errors). According to the relevant LTE test specification (3GPP TS 36.133 release 10.1.0 release 10) and other documents, the time synchronization error can be estimated assuming a uniform distribution of synchronization errors. One such estimate is equivalent to 200ns (100ns peak-to-peak). It should be noted that the voice over LTE (VoLTE) functionality also requires cellular network synchronization as low as 150 nanoseconds (75ns peak-to-peak) assuming uniform distribution of synchronization errors. Therefore, the time synchronization accuracy of LTE networks will continue to be assumed to be within 150 ns.

For range position accuracy, FCC mandate NG 911 specifies positioning accuracy requirements of 50 meters and 100 meters. However, for the Location Based Services (LBS) market, indoor location requires a more stringent reliability, i.e., 3 meters @ 67%. Thus, the range and positioning errors introduced by a 150ns time synchronization error (standard deviation of 43 ns) are much larger than a 3 meter range error (standard deviation of 10 ns).

While time synchronization of cellular networks may be sufficient to meet the mandatory FCC NG E911 emergency location requirements, the synchronization accuracy does not meet the demand for LBS or RTLS system users that require significantly more accurate location. Therefore, to support LBS and RTLS, there is a need in the art to reduce positioning errors due to lack of accurate time synchronization by cellular/wireless networks.

While LTE 4G deployments support Location Based Services (LBS) with the available location information for the terminal, the proposed 5G deployments will replace LBS support with location as a service (Laas) data transmission. According to this model, location data will be collected by the communication service operator and made available to the client through the API. This approach is intended to better protect the privacy of the physical location data of enterprise customers and consumers, but also presents some problems.

Positioning in a 5G network may be performed within the UE device (device centric) or in a network centric manner, or both (according to the LTE standard). However, the network-centric option has several advantages over other approaches. First, network-centric positioning enables more advanced positioning techniques to be utilized and updated, which have computing power far exceeding that available in the UE device. Network-centric positioning also makes it easier to upgrade the positioning algorithm and make other changes. Positioning within the UE device tends to create a logistics burden (for the carrier) because the carrier is dependent on the device manufacturer to effect the necessary changes and the device's computational resources are more limited. Furthermore, the network-centric option allows the positioning engine to run continuously in the background, enabling ubiquitous high-accuracy positioning, providing up-to-date location information with low latency, while also allowing location information to be obtained anywhere below the network coverage area, including indoors and within urban corridor environments. Also, network-centric positioning is more energy efficient from a device perspective, since network-centric selection reduces the heavy computational burden required for UE positioning; thereby achieving significant UE power consumption improvements.

In particular, UE power consumption improvement is very important for wireless modems for internet of things (IoT) applications. These modems may also employ additional (for network-centric positioning) strategies in order to conserve power.

Generally, wireless networks utilize two positioning methods: uplink time difference of arrival (TDOA) and downlink OTDOA. The uplink method utilizes signals transmitted by the UE to the network element, and the downlink method utilizes signals transmitted from the network element to the UE. In particular, the network elements are various types of cells (macro cells, small cells, distributed cells, etc.) and the signals are reference signals. Each approach has its own trade-offs, and depending on the circumstances, one approach may be more favorable than the other. Both methods rely on a multipoint positioning method. However, in the case of downlink OTDOA, the cell tower transmission power is two orders of magnitude higher than the UE transmitter power. Thus, the downlink OTDOA approach provides more general coverage. Meanwhile, uplink positioning may also support angle of arrival (AoA)/direction of arrival (DoA) methods, where AoA/DoA is measured based on uplink transmissions from UEs and known configuration of eNodeB (also known as eNoB/eNB) antenna arrays. This approach has the potential for higher positioning accuracy, but the accuracy depends to a large extent on the characteristics of the particular cell antenna. On the other hand, uplink TDOA and AoA methods are inherently network centric in that the relevant UE transmissions can be received and collected by a network element, e.g., eNoB.

Conventional implementations of downlink OTDOA are UE-assisted (e.g., 3GPP 36.305v14) or UE-based. In the UE-assisted scheme, the UE must perform downlink timing measurements. These measurements involve estimating the Reference Signal Time Difference (RSTD) between reference signals from several cells. These time differences are then reported to a network element called an evolved serving mobile location center (E-SMLC) for further processing. Therefore, a number of techniques are needed to mitigate downlink timing measurement errors caused by RF propagation phenomena. Otherwise, the UE positioning accuracy may be severely affected. However, mitigating such errors requires the adoption of state-of-the-art positioning signal processing techniques, at the cost of a heavy computational burden on the UE resources. Therefore, there is a need to improve downlink OTDOA performance without requiring the UE to have larger computational resources and without placing a logical burden on the carrier and UE manufacturers.

At the same time, a network-centric architecture is needed to enable location-as-a-service (Laas) data transmission, and such an architecture is needed to enable advanced functions that are not feasible before e.g. joint uplink/downlink or downlink/uplink UE positioning, which improves positioning system reliability and location fix accuracy. There is also a need for such an architecture to enable multiple positioning techniques to be performed on the same data simultaneously and to enable this functionality. Finally, such an architecture needs to allow more towers and infrastructure to be seen, as the entire database of all of the network elements is available.

Wireless devices, also known as User Equipment (UE), utilize various technologies including GPS, cellular signals, Wi-fi (wlan) signals, beacon signals, and various passive and active sensors such as gyroscope sensors to obtain remote location information. Since a single technology cannot address all geolocation environment and/or performance requirements, multiple technologies are combined into hybrid positioning (also referred to as fused positioning).

Furthermore, there are a variety of geolocation methods and/or techniques, such as uplink, downlink, and enhanced cell ID positioning; assisted GNSS/GPS, triangulation, three-point positioning, multi-point positioning, RF fingerprinting, Received Signal Strength (RSS), etc.

While not all existing wireless communication networks integrate multiple location technologies, the integration work is highest in cellular communication networks. However, the geolocation architecture and functionality of these networks is technology/method/technology specific, thus limiting the ability to comply with changing industry/customer requirements and/or take advantage of the advantages of geolocation technologies and methods/technologies including hybrid/converged positioning.

This architectural fragmentation is caused by current cellular network policies that tend to be low bandwidth when communicating with the location server. This explains the popularity of past probabilistic positioning algorithms and methods, including RF fingerprinting, that collect multiple Received Signal Strength Indicators (RSSI) for each network node visible at the location and send this information (data) to a positioning server for use in determining a target location fix. Over time, higher performance levels, including indoor performance (the wireless E911 position accuracy requirement of the fourth report and order from the Federal Communications Commission (FCC) in the united states), are required by clients and applications. This exposes RF fingerprint limitations such as reduced accuracy in object movement scenes, the continuing need for laborious human involvement to build and update so-called fingerprint maps, and the presence of positioning ambiguities and error floor phenomena.

In response to the above-described need for greater positioning accuracy and reliability, the wireless industry has deployed methods and techniques with the potential for precise positioning. In wireless communication networks, the most widely used are downlink and uplink positioning, which includes time of arrival (TOA), time difference of arrival (TDOA), time of flight (TOF), angle of arrival (AOA), and received signal phase techniques.

The potential for better accuracy is also accompanied by higher computational requirements. However, according to the low bandwidth approach, existing wireless communication network architectures require wireless devices and/or specific (dedicated) network elements/components to determine TOAs, TDOAs (also known as RSTD/RTOA), TOF, AOAs, etc., and their metrics, i.e., observations, to be sent to a network location server responsible for location calculations using these observations. This overburdens the computing resources of the wireless device and/or particular network components such that the implementation of ever-advancing geolocation algorithms/methods is inhibited (due to constraints on computing resources), thereby limiting further improvements in location performance. It should be noted that the computational constraints of a wireless device or a specific (dedicated) network element are caused by limited size, cost, power consumption requirements, and logistical burdens and HW/SW legacy limitations.

Because a single location technology/method cannot be used to meet the needs of a client/application, the wireless industry is adopting a combination of two or more technologies/methods, i.e., a hybrid approach. However, hybrid positioning performance gains are incremental in nature and cannot meet the needs of all clients/applications. For example, combining GPS/GNSS and LTE DL OTDOA improves geolocation in outdoor environments, but does not improve geolocation in indoor environments in such hybrid approaches because GPS/GNSS does not operate in indoor environments.

At the same time, hybrid positioning increases cost, increases power consumption and increases solution complexity. Emerging internet of things (IoT) applications require wireless devices (sensors) with the following features: very low cost, small size, very low power consumption, low complexity, wide coverage and deep inside buildings. This is especially true for Cat-M and NBIOT IOT based solutions where battery life is very important and the device typically does not have a robust on-board processor. Other challenges include longer sleep times, shorter Time To First Fix (TTFF), and narrow bandwidth of the signal, which affect geolocation accuracy. Subsequently, hybrid solutions cannot be used due to the additional cost, power consumption, and large size. Furthermore, to mitigate the impact of the combination of narrow signal bandwidth and deep environment inside the building, advanced algorithms are needed that require a large amount of computing power/resources that are not reachable by IoT wireless devices. Therefore, there is not enough location solution for low cost, long battery life.

Current WLAN Access Points (APs) are characterized by multiple antennas at the transmitter and receiver and signal structures to enable advanced positioning. Thus, it is contemplated to utilize both Wi-Fi and LTE signals for indoor environments using hybrid positioning. However, advanced Wi-Fi target location is performed by the WLAN infrastructure, whereas LTE target location is typically split between the wireless device and the LTE infrastructure location server. Additionally, a Wi-Fi and LTE hybrid would require additional processing to combine the intermediate Wi-Fi and LTE results to complete the target location.

One option is for the wireless device to perform all intermediate Wi-Fi and LTE calculations, sending the results to the LSU server to determine the target location. Another option is for the wireless device to perform all calculations to determine its own location. Both options are not suitable for supporting advanced geolocation techniques, given the constraints of the wireless device computing resources.

Yet another option is for the WLAN infrastructure to provide intermediate information and/or other positioning information to the LSU to determine the location of the wireless device. However, this option imposes computational and logical (algorithm/and processing updates, WLAN and LTE coordination, etc.) burdens on the WLAN infrastructure.

GPS/GNSS positioning technology is an important part of wireless device positioning solutions. GPS/GNSS can fail in indoor and dense urban environments, while its accuracy is superior in rural and suburban environments. Furthermore, assisted GPS/GNSS (AGPS/AGNSS) methods (models) extend GPS/GNSS capabilities to many dense urban environments despite their reduced accuracy/reliability. The cellular network provides the necessary assistance data to the GPS/GNSS receiver equipped wireless device so that it can operate in AGPS/AGNSS mode. These receivers are also responsible for determining the observations: pseudoranges, pseudo-doppler, etc., which are transmitted to a location server of the network that performs the location calculations. Alternatively, the wireless device (GPS/GNSS) receiver can operate independently, i.e., with or without the assistance of a cellular network, can calculate its own position.

Thus, network assisted GPS/GNSS operation relies on signaling between the GPS/GNSS receiver of each wireless device and the network infrastructure, and at the same time requires significant computational resources for computing observations and their metrics; and in the standalone mode of operation, even more computing resources are required to compute the observations and the location of the device itself. Either option increases the cost, size and power consumption of the receiver.

Other technologies such as bluetooth, terrestrial beacon systems, etc. are also used for positioning in wireless networks. When integrated into a cellular network, these technology-specific functions may be spread out across wireless devices and network elements due to the aforementioned low communication bandwidth policies for wireless device geolocation. This hinders future advances in wireless network location performance, fails to meet client/application performance requirements, and places heavy computational loads on wireless devices and/or network components, logistical loads on the network and new advanced algorithm support creating HW/SW legacy limitations.

Advanced wireless network architectures, such as 5G (unified and more powerful new radio, global 3GPP standard), envisage location as a service (LaaS) data transport, whereby wireless devices act as gateways to the computing cloud and LaaS, dedicated to protected physical location data. However, due to the above drawbacks and physical location data security issues, current geo-location architecture fragmentation is not suitable for LaaS support.

Therefore, there is a need in the art for a unified framework/platform (architecture, functional entities and operations) that should accommodate positioning technologies, methods/technologies including all wireless devices, wireless signals, and including hybrid/converged positioning. Key benefits of this unified framework/platform are excellent positioning accuracy, reliability, energy efficient wireless devices, location as a service (LaaS) data transmission, enhanced security, promotion of new feature development, reduction of logical burden and traditional limitations; scalable computing power greatly exceeds the computing power available in mobile devices and/or infrastructure elements of a single network.

Disclosure of Invention

The present disclosure is directed to methods and systems for Radio Frequency (RF) based identification, object tracking, and location, including real-time location services (RTLS) systems that substantially obviate one or more of the disadvantages associated with existing systems. The method and system may use partially synchronized (in time) receivers and/or transmitters. According to an embodiment, RF-based tracking and positioning is implemented in a cellular network, but may also be implemented in any wireless system and RTLS environment. The proposed system may use software implemented digital signal processing and software defined radio technology (SDR). Digital Signal Processing (DSP) may also be used.

One approach described herein employs receiver clusters and/or transmitter clusters that are precisely time synchronized within each cluster, while inter-cluster time synchronization may be less accurate or not necessary at all. The present embodiments are applicable in all wireless systems/networks and include a simple mode of operation, a half-duplex mode of operation, and a full-duplex mode of operation. The embodiments described below operate with wireless networks employing various modulation types, including OFDM modulation and/or derivatives thereof. Thus, the embodiments described below operate with LTE networks, and the embodiments are also applicable to other wireless systems/networks.

As described in one embodiment, implementing RF-based tracking and positioning on a 3GPP LTE cellular network would significantly benefit from a precisely synchronized (in time) cluster of receivers and/or transmitters. The proposed system may use software and/or hardware implemented digital signal processing.

As described in embodiments, the network-centric architecture supports LaaS data transport, and is designed for 5G and other networks when all signal processing and location estimation is done in the cloud (i.e., outside the UE and/or eNodeB). There are many options for how all signal processing and position estimation can be done. In terms of uplink positioning, relevant UE reference signal transmissions may be collected/pre-processed by enodebs in the macro environment or LMUs in other environments and forwarded to a positioning server unit (LSU) for further processing and UE position determination. In the case of downlink (OTDOA), the tasks of collecting and pre-processing downlink reference signals may be performed by the UE. The UE may then send the collected downlink data to the LSU. In the case of downlink (OTDOA), the UE may also use the control plane and/or LTE user (data) plane to handle communications with the LSU. Thus, the signaling may conform to the OMA Secure User Plane Location (SUPL) protocol and/or 3GPP, such as the LTE Positioning Protocol (LPP). In the case of uplink AoA/DoA, the eNodeB may use LPPA and slmapp protocols (SLm application protocol) or MTTQ to handle communication with LSUs.

As described in the embodiments, this network-centric architecture may enable advanced features that were previously not feasible. These advanced features include: (a) determining the distance between the serving cell/tower and the UE using downlink OTDOA and determining the UE position using uplink AoA/DoA while also mitigating the impact of OTDOA synchronization error on the uplink/downlink UE positioning by using tracking algorithms/techniques; (b) utilizing tracking algorithms/techniques to improve UE positioning where an uplink AoA/DoA plus Round Trip Time (RTT) (also known as timing advance (TA or TADV)) method is used to determine UE position; and (c) utilizing tracking algorithms/techniques to estimate and correct/reduce synchronization errors in downlink OTDOA positioning methods.

As described in the embodiments, a downlink (OTDOA) UE positioning method includes a navigation processor that utilizes multipoint positioning techniques/methods (also known as hyperbolic navigation). This multipoint location technique requires solving a plurality of hyperbolic (RSTD/TDOA) equations. There are iterative methods and non-iterative (closed form) solutions. In one embodiment, a hybrid method is described that separates a plurality of available (audible) reference points (base stations) into sets of three RSTD/TDOA subsets and finds a closed-form solution for each subset. Thereafter, the location fix may be completed using a location consistency algorithm. In a second embodiment, a combination of iterative and non-iterative solutions of RSTD/TDOA values from the same group may be utilized to improve location bearing. In a third embodiment, an initial position estimate for the UE position may be determined based on iterative and non-iterative algorithms by utilizing the uplink AoA/DoA estimate and RTT.

As described in the embodiments, the wireless network environment sometimes does not support the multipoint positioning method, which requires at least three reference points (for 2-D positioning) to obtain a positioning azimuth, because only two high power cells are used to transmit RF signals to a large area. Thus, when RTT is available, the UE is located along an arc defined by the serving sector azimuth beam width and RTT/2 range. When AoA/DoA estimates are available, the UE location may be determined near the intersection of the hyperbola and the arc. Both methods may also be used. When neither RTT nor AoA/DoA estimates are available, the UE location is determined by scoring the intersection point corresponding to each cell/tower (sector) on the selected hyperbola, each tower (sector) will be scored. The score is based on the angular difference between the direction the cell points and the direction to the point on the hyperbola and the distance from each point to the corresponding cell/tower. The scores may be weighted according to the SNR of their corresponding cell/tower.

As described in an embodiment, the LSU may include a communication processor configured for signaling and information exchange with the UE, eNodeB, and/or network element. The signaling may conform to the OMA SUPL protocol and/or 3GPP LPP/LPPa, or a combination of LPP, LPPa, and SUPL, as well as other protocols used or available for communication with the network, such as the LCS-AP protocol.

As described in an embodiment, the LSU component may be instructions stored in a memory and configured to execute on a processor of a 4.5G MEC (mobile edge computing) server located at an edge of a communication network. The LSU component may be integrated as a managed application on a 4.5G MEC. In a 5G deployment, LSU components may be hosted in a core network computing cloud. LSUs hosted in the core network computing cloud may support LaaS data transport, whereby the UE acts as a gateway for the core network computing cloud and LaaS, dedicated to protected physical location data.

As described in an embodiment, the LSU may include a downlink signal processor as well as an uplink signal processor and a navigation processor.

As described in embodiments, the unified framework/platform simultaneously supports locating wireless devices in an environment comprised of one or more wireless communication networks (e.g., cellular, WLAN, etc.) and/or one or more dedicated positioning systems (e.g., GPS/GNSS, terrestrial beacon systems, etc.).

As described in the embodiments, the unified framework/platform performs all of all signal processing, positioning, tracking and navigation tasks outside of the wireless device and/or specific (dedicated) network elements (WLAN AP, eNodeB, LMU, E-SMLC, etc.), while the wireless device and/or network elements are responsible for collecting and pre-processing snapshots of signals for wireless device positioning, tracking and navigation.

As described in the embodiments, all of the signal processing, localization, tracking, and navigation are performed in the LSU, while the pre-processing of the signal snapshot includes sending snapshot data to the LSU and, optionally, performing a compression operation to reduce the amount of snapshot data to be sent to the LSU.

As described in the embodiments, the LSU:

1. may be deployed within the network infrastructure or operator's IP services network;

2. may be deployed on a server at an edge facility of a cloud computing-based centralized radio access network (C-RAN) baseband processing, where the LSU may be integrated as a hosted application.

3. May be hosted in a computing cloud of the network and/or a service network cloud of the operator via a secure remote internet connection.

4. May be a fully hosted and managed cloud service that is connected to one or more operators' network infrastructure and/or IP services networks via a secure remote internet connection.

5. May be hosted by an independent private network such as CBRS, licensed assisted access network, or WLAN network.

The latter three embodiments support LaaS data transmission.

With respect to wireless networks, downlink communication refers to whenever a wireless device receives a signal from one or more network nodes (cell sites, WLAN APs, satellites, etc.). Meanwhile, uplink communication refers to whenever one or more network nodes (cell base stations, WLAN APs, etc.) and/or dedicated infrastructure elements (LMUs) receive a signal transmitted from or by a wireless device. The wireless device location process using downlink signals is referred to as downlink location. Similarly, the positioning process using the uplink signal is referred to as uplink positioning.

With respect to locating a particular network (system), signals used for wireless device location are typically on the downlink or uplink, such as GPS/GNSS, terrestrial beacon systems, and the like.

As described in the embodiments, the unified framework/platform:

1. the burdensome computational burden on the wireless device and/or on certain (dedicated) network elements is reduced.

2. Enabling LaaS for current and future network architectures/environments.

Enabling advanced functions that were previously not feasible (using existing architectures), such as joint uplink/downlink or downlink/uplink positioning, to improve the reliability and position fix accuracy of the positioning system.

4. Providing scalable (virtually unlimited) computational bandwidth and resources, supporting new advanced positioning technologies.

5. The logistical burden associated with multiple network management and interfaces, new technology upgrades and HW/SW legacy restrictions is alleviated.

6. The machine learning can improve the positioning reliability and the position and orientation accuracy.

7. The wireless device location process may run continuously in the background.

8. Historical data and crowd sourcing are utilized.

Thus, a generally high accuracy positioning is enabled that provides up-to-date location information with low latency without compromising wireless device power consumption.

As described in an embodiment, the LSU may include one or more of the signal processors operating on snapshot data from one or more wireless devices and/or one or more network-specific (dedicated) elements.

As described in an embodiment, one or more of the signal processors of the LSU operate on downlink snapshot data and/or uplink snapshot data.

As described in embodiments, the LSU further comprises a navigation processor (also referred to as a positioning engine) -receiving the output from the one or more signal processors and performing position estimation and position tracking calculations by utilizing a variety of positioning (geolocation) methods/techniques/technologies (including a combination of several methods, techniques and technologies, i.e., hybrid positioning).

As described in the embodiments, the LSU signal processor and the positioning engine also receive assistance/assistance information.

As described in an embodiment, the LSU signal processor may be part of a positioning engine.

As described in an embodiment, the LSU may include a communication processor configured for signaling and information exchange with the wireless device and/or one or more networks and network-specific (dedicated) elements within one or more networks. For example, the signaling may conform to the OMA SUPL protocol and/or the 3GPP LPP/LPPa protocol, MTTQ, and/or the like.

Additional features and advantages of the embodiments will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the embodiments. The advantages of such embodiments will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the embodiments as claimed.

Drawings

The accompanying drawings, which are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification, illustrate embodiments and together with the description serve to explain the principles of the embodiments. In the drawings:

fig. 1 and 1A illustrate narrow bandwidth ranging signal frequency components in accordance with an embodiment;

figure 2 illustrates exemplary wide bandwidth ranging signal frequency components;

3A, 3B, and 3C illustrate block diagrams of master and slave units of an RF mobile tracking and location system according to an embodiment;

figure 4 shows an embodiment of synthesizing a wideband baseband ranging signal;

fig. 5 illustrates elimination of signal precursors by elimination according to an embodiment;

fig. 6 illustrates precursor cancellation with fewer carriers, in accordance with an embodiment;

FIG. 7 illustrates an embodiment of a one-way transfer function phase;

FIG. 8 illustrates an embodiment of a positioning method;

fig. 9 shows LTE reference signal mapping;

figure 10 illustrates an embodiment of an enhanced cell ID + RTT positioning technique;

FIG. 11 illustrates an embodiment of an OTDOA positioning technique;

fig. 12 illustrates operation of a time observation unit (TMO) installed at an operator's eNB facility, according to an embodiment;

fig. 13 illustrates an embodiment of a wireless network location device diagram;

fig. 14 illustrates an embodiment of a wireless network location downlink ecosystem for enterprise applications;

FIG. 15 illustrates an embodiment of a wireless network location downlink ecosystem for network-wide applications;

fig. 16 illustrates an embodiment of a wireless network location uplink ecosystem for enterprise applications;

FIG. 17 illustrates an embodiment of a wireless network location uplink ecosystem for network-wide applications;

fig. 18 illustrates an embodiment of an UL-TDOA environment that may include one or more DAS and/or femto/small cell antennas;

fig. 19 shows an embodiment of UL-TDOA similar to fig. 18, which may include one or more cell towers that may be used in place of DAS base stations and/or femto/small cells;

fig. 20 shows an embodiment of cell level positioning;

figure 21 shows an embodiment of serving cell and sector ID positioning;

Fig. 22 shows an embodiment of E-CID plus AoA positioning;

fig. 23 shows an embodiment of AoA localization;

FIG. 24 shows an embodiment of TDOA with wide and close range between the receiving antennas;

FIG. 25 illustrates an embodiment of a three sector deployment;

fig. 26 illustrates an embodiment of antenna port mapping;

FIG. 27 illustrates an embodiment of LTE release 11U-TDOA location technique;

figure 28 shows an embodiment of a high-level block diagram of a multi-channel Location Management Unit (LMU);

fig. 29 shows an embodiment of DL-OTDOA techniques in a wireless/cellular network with a location server;

FIG. 30 illustrates an embodiment of U-TDOA technique in a wireless/cellular network with a location server;

FIG. 31 shows a pictorial embodiment of a rack-type enclosure;

figure 32 illustrates an embodiment of a high-level block diagram of a plurality of single-channel LMUs clustered (integrated) in a rack enclosure;

figure 33 shows an embodiment of a high level block diagram of multiple small cells with integrated LMUs clustered (integrated) in a rack enclosure (one-to-one antenna connection/mapping); and

FIG. 34 illustrates an embodiment of a high-level block diagram of an LMU and DAS integration;

FIG. 35 illustrates an embodiment of a high-level block diagram of an LMU and WiFi infrastructure integration.

Fig. 36 shows an embodiment of a diagram of UE location fixes in a "two tower" environment.

Fig. 37 shows timing advance adjustment.

Fig. 38 shows an eNB and UE reception-transmission time difference.

FIG. 39 shows an architecture for locating a UE within an E-UTRAN.

Fig. 40 illustrates an embodiment of a wireless network system architecture for UE positioning.

Fig. 41 shows an alternative embodiment of a wireless network system architecture for UE positioning.

Fig. 42 illustrates a current multi-network/multi-type access node environment.

Fig. 43A shows the current LTE EPC architecture.

Fig. 43B shows a direct connection of an LSU embodiment.

Fig. 43C illustrates the connection of an LSU embodiment via an IP service network.

Fig. 43D shows an E-SMLC with relay entities.

Fig. 44 illustrates an embodiment of a unified framework/platform.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings.

The present embodiments relate to methods and systems for RF-based identification, object tracking and localization (including RTLS). According to an embodiment, the method and system employ a narrow bandwidth ranging signal. This embodiment operates in the VHF band but may also be used in the HF, LF and VLF bands as well as in the UHF band and higher. This embodiment employs a multipath mitigation processor. The use of a multipath mitigation processor improves the accuracy of tracking and positioning achieved by the system.

This embodiment includes a small, highly portable base unit that allows a user to track, locate and monitor multiple persons and multiple objects. Each unit has its own ID. Each unit broadcasts an RF signal with its ID and each unit can send back a return signal that can include its own ID as well as voice, data and additional information. Each unit processes return signals from other units and continuously determines the relative and/or actual position of these signals according to triangulation or trilateration and/or other methods used. These embodiments can also be easily integrated with products such as GPS devices, smart phones, two-way radios, and PDAs. The resulting product will have all of the functionality of a stand-alone device while utilizing the processing power of existing displays, sensors (such as altimeters, GPS, accelerometers, and compasses), and their hosts. For example, a GPS device with the device technology described herein would be able to provide the user's location on a map and map the locations of the other members of the group.

As integrated circuit technology improves, the size of implementations based on FPGA implementations is between about 2 x 4 x 1 inches and 2 x 0.5 inches or less. Depending on the frequency used, the antenna will be integrated into the device or protrude through the device housing. An Application Specific Integrated Circuit (ASIC) based version of the device would be able to incorporate most of the functionality of FPGAs and other electronic components in a unit or tag. An ASIC-based standalone version of the product would allow device sizes of 1 x 0.5 inches or less. The antenna size will be determined by the frequency used and a portion of the antenna may be integrated into the housing. ASIC-based implementations are designed to be integrated into products that may consist of chipsets only. There should not be any significant physical size differences between the master unit or the tag unit.

The device may handle the multipath mitigation algorithm using standard system components (off-the-shelf components) operating at multiple frequency ranges (bands). Software for digital signal processing and software defined radios may be used. The signal processing software, in combination with minimal hardware, allows the assembly of radio components with software defined transmit and receive waveforms.

Us patent No. 7561048 discloses a narrow bandwidth ranging signal system whereby a narrow bandwidth ranging signal is designed to fit into a low bandwidth channel, for example using a voice channel that is only a few kilohertz wide (although some of the low bandwidth channels may extend into tens of kilohertz). This is in contrast to conventional positioning systems that use channels that are hundreds of kilohertz to tens of megahertz wide.

The advantages of the narrow bandwidth ranging signal system are as follows: 1) at lower operating frequencies/frequency bands, conventional positioning system ranging signal bandwidths exceed carrier (operating) frequency values. Thus, such systems cannot be deployed over LF/VLF and other lower frequency bands (including HF). Unlike conventional positioning systems, the narrow bandwidth ranging signal system described in U.S. patent No. 7561048 can be successfully deployed over LF, VLF, and other bands because the system ranging signal bandwidth is much lower than the carrier frequency value; 2) at the lower end of the RF spectrum (some VLF, LF, HF and VHF bands, e.g. up to the UHF band), conventional positioning systems cannot be used because the FCC severely limits the allowed channel bandwidth (12kHz-25kHz), which makes the use of conventional ranging signals impossible. Unlike conventional positioning systems, the ranging signal bandwidth of a narrow bandwidth ranging signal system completely complies with FCC regulations and other international spectrum regulatory agencies; and 3) it is well known that a narrow bandwidth signal is independent of the operating frequency/bandwidth, as compared to a wide bandwidth signal, which inherently has a higher signal-to-noise ratio (SNR) (see Ray H. Hashmemi, William G. Bradley (2003). This increases the operating range of the narrow bandwidth ranging signal positioning system regardless of the frequency/band (including UHF band) in which it operates.

Thus, unlike conventional positioning systems, narrow bandwidth ranging signal positioning systems can be deployed at the lower end of the RF spectrum (e.g., VHF and lower bands, down to the LF/VLF band) where multipath phenomena are less pronounced. Meanwhile, a narrow bandwidth ranging positioning system can be deployed on the UHF band and higher bands, so that the SNR of ranging signals is improved, and the operating range of the positioning system is enlarged.

To minimize multipath (e.g., RF energy reflections), it is desirable to operate over the VLF/LF band. However, at these frequencies, the efficiency of portable/mobile antennas is very small (about 0.1% or less because the antenna length (size) is short relative to the RF wavelength). Furthermore, at these low frequencies, the noise level from natural and man-made sources is much higher than at higher frequencies/bands (e.g., VHF). Together, these two phenomena may limit the applicability of the positioning system, such as its operating range and/or mobility/portability. Thus, for certain applications where operating range and/or mobility/portability are important, higher RF frequencies/bands may be used, such as HF, VHF, UHF and UWB.

In the VHF and UHF bands, the noise level from natural and man-made sources is significantly lower than on the VLF, LF, and HF bands; and at VHF and HF frequencies, multipath phenomena (e.g., RF energy reflections) are less severe than at UHF and higher frequencies. In addition, at VHF the antenna efficiency is significantly better than at HF and lower frequencies, and at VHF the RF penetration is much better than at UHF. Thus, the VHF band provides a good compromise for mobile/portable applications. On the other hand, in some special cases, such as GPS, where VHF frequencies (or lower frequencies) cannot penetrate the ionosphere (or be deflected/refracted), UHF may be a good choice. In any case (and in all cases/applications), however, a narrow bandwidth ranging signal system would have advantages over conventional wide bandwidth ranging signal location systems.

The actual application will determine the exact specification (such as power, emissions, bandwidth, and operating frequency/band). Narrow bandwidth ranging allows users to receive licensed or unlicensed, or use unlicensed bands set forth in the FCC, because narrow band ranging allows operation over many different bandwidths/frequencies (including the strictest narrow bandwidths set forth in the FCC: 6.25kHz, 11.25kHz, 12.5kHz, 25kHz, and 50kHz) and conforms to the corresponding technical requirements of the appropriate sector. Thus, multiple FCC zones and license exemptions within such zones would be applicable. The main FCC regulations applicable are:part 90 of chapter 47 of federal regulations, private land mobile radio service, part 94 of chapter 47 of federal regulations, personal radio service,part 15 of chapter 47 of federal regulations, radio frequency equipment. (by comparison, broadband signals in this context are a few hundred kilohertz up to 10 to 20 MHz.)

Generally, forparts 90 and 94, the VHF implementation allows users to operate the device at up to 100mW under certain exceptional circumstances (e.g., low power radio service). For some applications, the allowed transmission power in the VHF band is between 2 and 5 watts. For 900MHz (UHF band), the allowed transmission power is 1W. At frequencies from 160kHz to 190kHz (LF band), the allowed transmission power is 1 watt.

Narrowband ranging can conform to many, if not all, different spectral permits and allow accurate ranging while still conforming to the most stringent regulatory requirements. This applies not only to the FCC, but also to other international organizations that regulate spectrum usage worldwide (including europe, japan, and korea).

The following is a list of common frequencies used, typical powers used, and distances that a tag can communicate with another reader in a real-world environment (see Inwood Production and wavelet Dan Dobkin, WJcommunications, V1.47/10/02):

the proposed system operates at VHF frequencies and employs proprietary methods to transmit and process RF signals. More specifically, the proposed system uses DSP technology and Software Defined Radio (SDR) to overcome the limitations of narrow bandwidth requirements at VHF frequencies.

Operating at lower (VHF) frequencies reduces scattering and provides better wall penetration. The net result is that the range of frequencies mentioned above is increased by about ten times over the range of commonly used frequencies. For example, the measurement range of the prototype is compared with the measurement ranges of the RFID technology listed above:

216MHz 100mw 700feet

with narrow-band ranging techniques, the range of frequencies in use, the typical power used, and the distance that a tag will be able to communicate with another reader in a real-world environment will increase significantly:

The battery loss depends on the design, the transmission power and the duty cycle of the device, e.g. the time interval between two consecutive distance (position) measurements. In many applications, the duty cycle is large, 10X to 1000X. In applications where the duty cycle (e.g., 100X) is large, a version of the FPGA transmitting 100mW of power will have a rise time of about three weeks. The rise time is expected to increase by 10X based on the version of the ASIC. Additionally, ASICs inherently have lower noise levels. Thus, the ASIC based version may also increase the operating range by approximately 40%.

Those skilled in the art will appreciate that this embodiment does not compromise the long operating range of the system while significantly improving positioning accuracy in RF challenging environments (e.g., buildings, city corridors, etc.).

Generally, tracking and positioning systems employ a tracking-positioning-navigation method. These methods include time of arrival (TOA), time difference of arrival (DTOA), and a combination of TOA and DTOA. Time of arrival (TOA) as a distance measurement technique is generally described in U.S. patent No. 5525967. The TOA/DTOA-based system measures the RF ranging signal direct line-of-sight (DLOS) time-of-flight (e.g., time delay) and then converts it to a range.

In the case of RF reflections (e.g., multipath), multiple copies of the RF ranging signal with various delay times are superimposed onto the DLOS RF ranging signal. Tracking and positioning systems using narrow bandwidth ranging signals cannot distinguish between DLOS signals and reflected signals without multipath mitigation. These reflected signals therefore cause errors in the estimated range signal DLOS time of flight, which in turn affects the range of estimation accuracy.

This embodiment advantageously uses a multipath mitigation processor to separate the DLOS signal and the reflected signal. Thus, this embodiment significantly reduces the error in the estimated time of flight of the ranging signal DLOS. The proposed multipath mitigation method can be used for all RF bands. It may also be used with wide bandwidth ranging signal positioning systems. And the multipath mitigation method may support various modulation/demodulation techniques including spread spectrum techniques such as Direct Spread Spectrum (DSS) and Frequency Hopping (FH).

In addition, noise reduction methods may be applied in order to further improve the accuracy of the method. These noise reduction methods may include, but are not limited to, coherent summing, non-coherent summing, matched filtering, time diversity techniques, and the like. The residuals of multipath interference errors may be further reduced by applying post-processing techniques such as maximum likelihood estimation (e.g., viterbi algorithm), minimum variance estimation (kalman filter), and the like.

This embodiment can be used in systems with simple, half-duplex and full-duplex modes of operation. Full duplex operation imposes very high complexity, cost and logistical requirements on RF transceivers, which limits the system operating range in portable/mobile device implementations. In a half-duplex mode of operation, the reader (often referred to as a "master") and tag (sometimes also referred to as a "slave" or "target") are controlled by a protocol that only allows the master or slave to transmit at any given time.

The alternation of transmission and reception allows the use of a single frequency in the distance measurement. This arrangement reduces the cost and complexity of the system compared to a full duplex system. The simple mode of operation is conceptually simpler, but requires tighter synchronization of events between the master unit and the target unit, including the start of the ranging signal sequence.

In this embodiment, the narrow bandwidth ranging signal multipath mitigation processor does not increase the ranging signal bandwidth. The multipath mitigation processor advantageously uses different frequency components to allow propagation of a narrow bandwidth ranging signal. Further ranging signal processing may be performed in the frequency domain by employing super-resolution spectral estimation algorithms (MUSIC, rootMUSIC, ESPRIT) and/or statistical algorithms like RELAX, or by combining synthetic ranging signals with a relatively large bandwidth in the time domain and further processing the signals. The different frequency components of the narrow bandwidth ranging signal may be pseudo-randomly selected, may be contiguous or spaced apart in frequency, and may have uniform and/or non-uniform spacing in frequency.

This embodiment extends the multipath mitigation technique. The signal model for narrow-band ranging is a complex exponential (as described elsewhere herein) whose frequency is proportional to the delay defined by the range plus a similarity term whose delay is defined by the time delay associated with the multipath. The model is independent of the actual implementation of the signal structure (e.g., step frequency, chirp, etc.).

The frequency separation between the direct path and the multipath is nominally extremely small and normal frequency domain processing is insufficient to estimate the direct path range. For example, a step frequency ranging signal over 5MHz at a 100KHz step rate over a 30 meter (100.07 nanosecond delay) range results in a frequency of 0.062875 radians/sec. A multipath reflection with a path length of 35 metres will give a frequency of 0.073355 rad/s. The interval is 0.0104792. The frequency resolution of the 50 sample observable has a natural frequency resolution of 0.12566 Hz. Therefore, it is not possible to separate the direct path from the reflected path and accurately estimate the direct path range using conventional frequency estimation techniques.

To overcome this limitation, the present embodiment uses a unique combination of a subspace decomposition high resolution spectral estimation method and a specific implementation of multi-modal clustering analysis. Subspace decomposition techniques rely on the separation of the estimated covariance matrix of the observed data into two orthogonal subspaces, the noise subspace and the signal subspace. The theory of the subspace decomposition method is that the projection of the observable on the noise subspace contains only noise, and the projection of the observable on the signal subspace contains only signal.

The super-resolution spectrum estimation algorithm and the RELAX algorithm are able to distinguish densely placed frequencies (sinusoids) in the spectrum in the presence of noise. These frequencies are not necessarily harmonically related and, unlike the Digital Fourier Transform (DFT), the signal model does not introduce any artificial periodicity. For a given bandwidth, these algorithms provide significantly higher resolution than the fourier transform. Therefore, the direct line of sight (DLOS) can be reliably distinguished from other Multipaths (MPs) with high accuracy. Similarly, applying a thresholding method, which will be explained later, to the artificially generated synthetic wider bandwidth ranging signal enables DLOS to be reliably distinguished from other paths with high accuracy.

According to this embodiment, the multipath mitigation processor may employ Digital Signal Processing (DSP) to reliably distinguish DLOS from other MP paths. There are a number of super-resolution algorithms/techniques in the art of spectral analysis (spectral estimation). Examples include subspace-based approaches: multiple signal characterization (MUSIC) algorithm or root-MUSIC algorithm, signal parameter estimation via rotation invariance technique (ESPRIT) algorithm, Pissodisco Harmonic Decomposition (PHD) algorithm, RELAX algorithm, etc.

The mentioned super-resolution algorithm works on the premise that the signals impinging on the antennas are not fully correlated. As a result, performance is severely degraded in highly correlated signal environments, as may be encountered in multipath propagation. Multipath mitigation techniques may involve a pre-processing scheme known as spatial smoothing. Thus, the multipath mitigation process may make computation intensive and complex, i.e., increase the complexity of the system implementation. Multipath mitigation with lower system computational cost and implementation complexity can be achieved by using a super resolution Matrix Pencil (MP) algorithm. The MP algorithm is classified as a non-search procedure. Thus, the MP algorithm is computationally less complex and eliminates the problems encountered in search programs used in other super resolution algorithms. Furthermore, the MP algorithm is insensitive to correlated signals and requires only a single channel estimate, and can also estimate the delay associated with coherent multipath components.

In all of the above-described super-resolution algorithms, the input (i.e., received) signal is modeled as a linear combination of a complex exponential and its complex frequency amplitude. In the case of multipath, the received signal will be as follows:

wherein β × e^i2πf×tIs a transmission signal, f is an operating frequency, L is the number of multipath components, and

and τ_KRespectively the complex attenuation and propagation delay of the kth path. The multipath components are indexed such that propagation delays are considered in ascending order. Thus, in this model, τ₀Representing the propagation delay of the DLOS path. Obviously, tau₀The value is of most interest because it is all τ_KIs measured. Phase theta_KIt is generally assumed to be random from one measurement period to another, with a uniform probability density function U (0,2 pi). Therefore, we assume α_KConst (i.e., constant value)

Parameter alpha_KAnd τ_KIs a randomly time varying function that reflects the motion of people and equipment in and around buildings. However, since the rate of change of the above parameters is very slow compared to the measurement time interval, these parameters may be considered as not being time-invariant within a given measurement periodAnd (4) changing random variables.

All these parameters are frequency dependent in that they are related to radio signal characteristics such as transmission and reflection coefficients. However, in this embodiment, the operating frequency varies very little. Thus, the above parameters can be assumed to be independent of frequency.

Equation (1) can be expressed in the frequency domain as:

Wherein: a (f) is the complex amplitude of the received signal, (2 π × τ)_K) Is the artificial "frequency" to be estimated by the super-resolution algorithm, and the operating frequency f is an argument; alpha is alpha_KIs the K path amplitude.

In equation (2), (2 π × τ)_K) Super-resolution estimation and subsequent tau_KThe values are based on a continuous frequency. In practice, there are a limited number of measurements. Thus, the variable f will not be a continuous variable, but a discrete variable. Thus, the complex amplitude a (f) can be calculated as follows:

wherein

Is a discrete frequency f_nDiscrete complex amplitude estimates (i.e., measurements) of (a) c.

In the case of the equation (3),

can be interpreted as a sinusoidal signal or frequency f_nAmplitude and phase after propagation through a multipath channel. It is noted that all super-resolution algorithms based on spectral estimation require complex input data (i.e. complex amplitudes).

In some cases, the true signal data (e.g.,

) Into a complex signal (e.g., an analytic signal). This conversion may be achieved, for example, by using a hilbert transform or other methods. However, in the case of short distances, the value τ₀Very small, which results in very low (2 π τ)_K) "frequency".

These low "frequencies" create problems for implementation of the hilbert transform (or other methods). Further, if only amplitude values are to be used (e.g.,

) Then the number of frequencies to be estimated will not only include (2 π τ)_K) "frequency" also includes combinations thereof. In general, increasing the number of unknown frequencies affects the accuracy of the super-resolution algorithm. Therefore, reliable and accurate separation of the DLOS path from other Multipath (MP) paths requires complex amplitude estimation.

The following is for obtaining complex amplitude in the presence of multipath

And a description of multipath mitigation processor operation. It is noted that while the description focuses on the half-duplex mode of operation, it is readily extendable for the full-duplex mode. The simplex mode of operation is a subset of the half-duplex mode, but would require additional event synchronization.

In a half-duplex mode of operation, the reader (often referred to as a "master") and tag (also referred to as a "slave" or "target") are controlled by a protocol that only allows the master or slave to transmit at any given time. In this mode of operation, the tag (target device) acts as a transceiver. The tag receives the ranging signal from the reader (master), stores the ranging signal in memory, and then retransmits the signal back to the master after a certain time (delay).

Fig. 1 and 1A show examples of ranging signals. The exemplary ranging signal employs successive different frequency components. Only need to measure distanceThe signal bandwidth remains narrow and other waveforms including pseudo-random, frequency and/or time spaced or orthogonal, etc. may be used. In fig. 1, the duration T of each frequency component_fLong enough to obtain the narrow bandwidth properties of the ranging signal.

Fig. 2 shows another variant of a ranging signal with different frequency components. This further variant comprises a plurality of frequencies (f) transmitted over a long period of time₁、f₂、f₃、f₄、f_n) So that the respective frequencies are narrowband. Such a signal is more efficient, but it occupies a wide bandwidth, and a wide bandwidth ranging signal affects the SNR, which in turn reduces the operating range. In addition, such wide bandwidth ranging signals would violate FCC requirements for the VHF band or lower frequency bands. However, in some applications, the wide bandwidth ranging signal allows for easier integration into existing signal and transmission protocols. In addition, such signals reduce the tracking fix time.

These multiple frequencies (f)₁、f₂、f₃、f₄、f_n) The bursts may also be continuous and/or pseudo-random, spaced apart in frequency and/or time or orthogonal, etc.

The narrowband ranging mode will yield an accuracy in the form of instantaneous wideband ranging, while increasing the range over which this accuracy can be achieved, compared to wideband ranging. This performance is achieved because at a fixed transmission power, the SNR at the receiver of the narrowband ranging signal is greater (in the appropriate signal bandwidth) than the SNR at the receiver of the wideband ranging signal. The SNR gain approximates the ratio of the total bandwidth of the wideband ranging signal to the bandwidth of each channel of the narrowband ranging signal. This provides a good trade-off when very fast ranging is not required, for example for stationary and slowly moving objects such as people walking or running.

The master device and the tag device are the same and may operate in either master or transceiver mode. All devices include a data/remote control communication channel. These devices may exchange information and the master device may remotely control the tag device. In this example shown in fig. 1, during master (i.e., reader) operation, the multipath mitigation processor sends ranging signals to the tag devices, and after a certain delay, the master/reader receives repeated ranging signals from the tag devices.

Thereafter, the multipath mitigation processor of the master device compares the received ranging signal with the ranging signal originally transmitted from the master device, and

determining an estimate f for each frequency component in the form of amplitude and phase_n. Note that, in equation (3),

defined as a one-way ranging signal run. In this embodiment, the ranging signal makes a round trip. In other words, the ranging signal travels in two ways: from the master/reader to the target/slave and from the target/slave back to the master/reader. Thus, the round trip signal complex amplitude received by the master device may be calculated as follows:

there are many techniques available for estimating complex amplitude and phase values, including, for example, matched filtering

And

according to this embodiment, the complex amplitude determination is based on a Received Signal Strength Indication (RSSI) value derived from the master device and/or tag device receiver

The value is obtained. Phase value

By comparison by the reader/masterThe phase of the return baseband ranging signal to be received is obtained from the phase of the original baseband ranging signal (i.e., transmitted by the reader/master). Furthermore, since the master device and the tag device have independent clock systems, detailed explanation of the device operation is enhanced by analyzing the influence of clock accuracy on phase estimation errors. As described above, unidirectional amplitude

The value may be obtained directly from the target device/slave device. However, unidirectional phase

The values cannot be measured directly.

In this embodiment, the ranging baseband signal is the same as that shown in fig. 1. However, for simplicity, it is assumed herein that the ranging baseband signal consists of only two frequency components, each containing a different frequency (i.e., F)₁And F₂) A plurality of periods of a cosine wave or a sine wave. It is noted that F₁＝f₁And F₂＝f₂. The number of cycles in the first frequency component is L and the number of cycles in the second frequency component is P. Note that L may or may not be equal to P, since for T_fEach frequency component may have a different number of cycles. In addition, there is no time gap between each frequency component, and F₁And F₂Starting from an initial phase equal to zero.

Fig. 3A, 3B and 3C depict block diagrams of a master or slave (tag) unit of an RF mobile tracking and locating system. F_OSCRefers to the frequency of the device system clock (crystal oscillator 20 in fig. 3A). All frequencies generated within the device are generated by the system clock crystal oscillator. The following definitions are used: m is a master (unit); AM is a tag (target) device (unit). The tag device operates in a transponder mode and is referred to as a transponder (AM) unit.

In one embodiment, the apparatus consists of an RF front end and RF back end, a baseband and a multipath mitigation processor. The R isThe F back end, baseband and multipath mitigation processors are implemented in the FPGA 150 (see fig. 3B and 3C). The system clock generator 20 (see fig. 3A) oscillates at the following positions: f_OSC20 MHz; or ω_OSC＝2π×20×10⁶. This is the ideal frequency, since in practical devices the system clock frequency is not always equal to 20 MHz:

it is to be noted that it is preferable that,

and

it should be noted that F other than 20MHz may be used_OSCOutside frequencies without any impact on system performance.

The electronic composition of the two units (main unit and tag unit) is the same and the different modes of operation are software programmable. The baseband ranging signals are generated in digital format by theFPGA 150 of the master device (blocks 155 to 180) (see fig. 2B). The baseband ranging signal is composed of two frequency components, each frequency component containing a plurality of cosine or sine wave periods of different frequencies. Initially, t is 0, theFPGA 150 in the master device (fig. 3B) outputs digital baseband ranging signals to itsupconverter 50 via I/Q DACs 120 and 125.FPGA 150 with F₁The frequency starts and at time T₁Thereafter starting to generate for a duration T₂F of (A)₂Frequency.

Since the frequency of the crystal oscillator may be different from 20MHz, the actual frequency generated by the FPGA will be F₁γ^MAnd F₂γ^M. In addition, time T₁Will be T₁β^MAnd T₂Will be T₂β^M. Also assume that T₁,T₂,F₁,F₂Is in the relationship of F₁γ^M*T₁β^M＝F₁T₁And F₂γ^M*T₂β^M＝F₂T₂In which F is₁T₁And F₂T₂Are all integers. This means that F₁And F₂The initial phase is equal to zero.

Since all frequencies are clocked from thesystem crystal oscillator 20, the output of the master's baseband I/Q DACs 120 and 125 are as follows:

and

wherein

And

Is a constant coefficient. Similarly, the output frequencies TX _ LO and RX _ LO from frequency synthesizer 25 (LO signals ofmixers 50 and 85) may be represented by constant coefficients. These constant coefficients are the same for the master (M) and the repeater (AM) but differ in thesystem crystal oscillator 20 clock frequency of each device.

The master device (M) and the repeater (AM) operate in a half-duplex mode. The RF front end of the master device upconverts the baseband ranging signal generated by the multipath mitigation processor using a quadrature upconverter (i.e., mixer) 50 and transmits the upconverted signal. After transmitting the baseband signal, the master device switches from TX mode to RX mode using the RF front end TX/RX switch 15. The repeater receives the signal and down-converts the received signal using its RF front-end mixer 85 (generating the first IF) and ADC 140 (generating the second IF).

Thereafter, the second IF signal is digitally filtered in the transceiver RF back-end processor using adigital filter 190 and further down-converted to a baseband ranging signal using RF back-end quadrature mixer 200, digital I/Q filters 210 and 230,digital quadrature oscillator 220, andsummer 270. The baseband ranging signals are stored in thememory 170 of the repeater using the Ramdata bus controller 195 andcontrol logic 180.

The repeater then switches from RX mode to TX mode using the RF front-end switch 15 and at a certain delay t_RTXAnd then begins retransmitting the stored baseband signal. Note that the delay is measured in the repeater (AM) system clock. Therefore, the temperature of the molten metal is controlled,

the master device receives the transponder transmission and uses its RF back-end quadrature mixer 200, digital I/Q filters 210 and 230,digital quadrature oscillator 220 to down-convert the received signal back to a baseband signal (see fig. 3C).

Thereafter, the master device calculates F in the received (i.e., recovered) baseband signal using the multipath mitigationprocessor arctan block 250 and the phase comparison block 255₁And F₂The phase difference between them. The amplitude values are derived from the RF back-end RSSI block 240.

To improve the estimation accuracy, it is always desirable to improve the SNR of the amplitude estimates fromblock 240 and the SNR of the phase difference estimates from block 255. In one embodiment, a multipath mitigation processor calculates a ranging signal frequency component duration (T)_f) Amplitude and phase difference estimates for many time instances within. These values, when averaged, may improve SNR. The SNR improvement may be compared to

In a proportional order, where N is the number of instances in which the amplitude and phase differences are acquired (i.e., determined).

Another method of SNR improvement is by applying matched filter techniques over a period of time to determine the amplitude and phase difference. However, another approach would be by sampling the received (i.e., repeated) frequency band ranging signal frequency components and doing so for a period T ≦ T_fThe inner phase is integrated with respect to the original (i.e., transmitted by the master/reader) frequency components of the I/Q version of the frequency band ranging signal to estimate the phase and amplitude of the received (i.e., repeated) frequency components of the frequency band ranging signal. The effect of the integration is on the I/Q formatIs averaged over multiple instances of amplitude and phase. Thereafter, the phase and amplitude values may be converted from I/Q format

And

and (4) format.

Assuming that t is 0, the main baseband processor (both in FPGA 150) starts the baseband ranging sequence under the control of the main multipath processor.

Wherein T is_f≥T₁β^M。

The phase outputs at theDACs 120 and 125 of the master device are as follows:

note thatDACs 120 and 125 have internal propagation delays that are not dependent on the system clock,

similarly, thetransmitter circuit components 15, 30, 40 and 50 will introduce additional delay independent of the system clock,

thus, the phase of the RF signal transmitted by the master device can be calculated as follows:

the RF signal coming from the master device (M) undergoes a phase shift as a function of the multipath phenomenon between the master device and the tag device

The value depending on the frequency transmitted, e.g. F₁And F₂. Due to the limited (i.e., narrow) bandwidth of the RF portion of the receiver, a repeater (AM) receiver cannot resolve each path. Thus, after a certain time (e.g. 1 microsecond) (corresponding to a flight distance of about 300 meters), when all reflected signals have reached the receiver antenna, the following formula applies:

in a repeater (AM) receiver at a first downconverter (element 85), the signal phase output (e.g., first IF) is as follows:

note the propagation delay in the receiver RF section (elements 15 and 60 to 85)

Not dependent on the system clock. After passing through the RF front-end filter and amplifier (elements 95 to 110 and 125), the first IF signal is sampled by the RF back-end ADC 140. Assume that theADC 140 is under-sampling the input signal (e.g., the first IF). Thus, the ADC also functions as a down-converter, thereby generating a second IF. The first IF filter, amplifier and ADC increase the propagation delay time. At the ADC output (second IF):

in theFPGA 150, the second IF signal (from the ADC output) is filtered by the RF back-enddigital filter 190, further down-converted back to the baseband ranging signal by a third down-converter (i.e.,quadrature mixer 200,digital filters 230 and 210, and digital quadrature oscillator 220), and summed insummer 270 and stored inmemory 170. At the third downconverter (i.e., quadrature mixer) output:

Note the propagation delay inFIR section 190

Independent of the system clock.

At RX->After the TX delay, the stored (in memory 170) baseband ranging signal from the master device (M) is retransmitted. Note that RX->TX delay

When the signal from the repeater reaches the receiver antenna of the master device (M), the RF signal from the repeater (AM) undergoes another phase shift as a function of the multipath

As described above, this phase shift occurs after a certain period of time when all reflected signals have reached the receiver antenna of the master device:

in the master receiver, the signal from the transponder undergoes the same down-conversion process as in the transponder receiver. The result is that the recovered baseband ranging signal is initially transmitted by the master device.

For a first frequency component F₁：

For the second frequency component F2:

and (3) replacing:

wherein T is_{D_M-AM}Is the propagation delay through the master (M) and repeater (AM) circuits.

Wherein:

is the LO phase shift from the master (M) and repeater (AM) frequency mixers (including the ADC) at time t-0.

In addition: k_{SYN_TX}＝K_{SYN_RX_1}+K_ADC+K_{SYN_RX_2}

First frequency component F1:

first frequency component F1, and then:

second frequency component F2:

second frequency component F2, and then:

further replacing:

Where α is a constant value.

The final phase equation is then:

according to equation (5):

wherein i is 2, 3, 4 … … … … …; and is

Is equal to

For example, the difference at time instances t1 and t2

To find out

Difference, we need to know T_{D_M-AM}：

T_{D_M-AM}＝T_{LB_M}β^M+T_{LB_AM}β^AM+t_RTXβ^AM；

Wherein T is_{LB_M}And T_{LB_AM}Is the propagation delay through the master (M) and repeater (AM) TX and RX circuits, which is measured by placing the device in loopback mode. It is noted that the master and repeater devices may automatically measure T_{LB_M}And T_{LB_AM}(ii) a And we also know t_RTXThe value is obtained.

From the above formula and t_RTXValue, T can be determined_{D_M-AM}Thus, for a given t₁And t₂The product is

The values can be calculated as follows:

or, assume β^M＝β^AM＝1：

From equation (6), it can be concluded that at the operating frequency, the complex amplitude value of the ranging signal can be calculated by processing the returned baseband ranging signal.

Initial phase value

Can be assumed to be equal to zero because the subspace algorithm is not sensitive to constant phase offsets. If desired, time of arrival (TOA) may be calculated by determining the TOA using a narrow bandwidth ranging signal method as described in U.S. Pat. No. 7561048

The value (phase initialization value), which is incorporated herein by reference in its entirety. The method estimates a ranging signal round-trip delay equal to 2 × T_FLTβ^MAnd is and

the value can be calculated from the following equation:

or:

in one embodiment, the returned baseband ranging signal phase value

Calculated by thearctan block 250 of the multipath processor. To improve SNR, the multipath mitigation processor phase comparison block 255 computes-using equation (6A) for many cases n (n-2, 3, 4 … … … … …) -and then averages them to improve SNR. It is noted that 2 × 10^-6＜t_n＜T_f+T_{D_M-AM}；t_m＝t₁+T_f。

As is apparent fromequations 5 and 6, the recovered (i.e., received) band ranging signal has the same frequency as the original baseband signal transmitted by the master device. Thus, despite master (M) systemThe clock and repeater (AM) system clocks may be different, but there is no frequency translation. Since the baseband signal is composed of several frequency components, each component consisting of a number of sine wave periods, the phase and amplitude of the received ranging signal can also be estimated by: each frequency component of the received baseband signal is sampled using a corresponding frequency component of the original (i.e., transmitted by the master) baseband signal and is less than or equal to T at a period T_fThe resulting signal is integrated.

This operation generates complex amplitude values in I/Q format for the received ranging signals

It is noted that the individual frequency components of each baseband signal transmitted by the master device must be offset in time by T_{D_M-AM}. The integration operation produces the effect of averaging multiple instances of amplitude and phase (e.g., improving SNR). Note that the phase and amplitude values may be converted from I/Q format to

And

and (4) format.

This sampling, at a period T ≦ T_fInternally integrating and subsequent conversion from I/Q format to

And

the method of formatting may be implemented in the phase comparison block 255 in fig. 3C. Thus, whether the method based on the implementation of equation (5) or the alternative method described in this section is used depends on the design and implementation of block 255.

Frequency difference f despite narrow ranging signal bandwidth_n-f₁May be relatively large, for example, on the order of several megahertz. Therefore, the bandwidth of the receiver must be maintainedWide enough to pass all f₁:f_nA ranging signal frequency component. This wide receiver bandwidth affects the SNR. To reduce the effective bandwidth of the receiver and improve the SNR, the RF back-end processor in theFPGA 150 may filter the received ranging signal baseband frequency components through a digital narrow-band filter tuned for each individual frequency component of the received baseband ranging signal. However, this large number of digital filters (the number of filters being equal to the number of individual frequency components, n) places an additional burden on the FPGA resources, thereby increasing the cost, size, and power consumption of the FPGA resources.

In one embodiment, only two narrow bandwidth digital filters are used: one filter always for f₁The frequency components are tuned and another filter can be used for all other frequency components f₂:f_nThe tuning is performed. Multiple instances of the ranging signal are transmitted by the master device. Each example consists of only two frequencies: f. of₁:f₂；f₁:f₃.....；f₁:f_i.....；f₁:f_n. Similar strategies are possible.

Note that it is also possible to adjust the frequency synthesizer (e.g., change K) entirely_SYN) Keeping the baseband ranging signal components to only two (or even one) generates the remaining frequency components. It is desirable to generate LO signals for both the up-and down-converter mixers using Direct Digital Synthesis (DDS) techniques. For higher VHF band frequencies, this may place an undesirable burden on the transceiver/FPGA hardware. However, for lower frequencies, this may be a useful approach. An analog frequency synthesizer may also be used, but may require additional time to settle after changing the frequency. In addition, in the case of an analog synthesizer, two measurements must be made at the same frequency in order to eliminate phase offsets that may develop after changing the frequency of the analog synthesizer.

Actual T used in the above equation_{D_M-AM}Measured in both: master (M) and repeater (AM) system clocks (e.g., T)_{LB_AM}And t_RTX) Both counting in the repeater (AM) clock andand at T_{LB_M}The master (M) counts in the clock. However, when calculating

When, T_{LB_AM}And t_RTXThe measurement (counting) is performed in the master (M) clock. This introduces an error:

the phase estimation error (7) affects the accuracy. Therefore, it is necessary to minimize the error. If beta is^M＝β^AMIn other words, all master and transceiver (tag) system clocks are synchronized, then the clock from t is eliminated_RTXThe contribution of time.

In one embodiment, the master unit (device) and the repeater unit (device) are able to clock in synchronization with any of the devices. For example, the master device may be used as a reference. Clock synchronization is achieved by using a remote control communication channel to adjust the frequency of the temperature compensatedcrystal oscillator TCXO 20 under control of theFPGA 150. While the selected transponder device is transmitting a carrier signal, a frequency difference is measured at the output of thesummer 270 of the master device.

Thereafter, the master device sends a command to the repeater to increase/decrease the TCXO frequency. This process may be repeated several times to achieve greater accuracy by minimizing the frequency at the output ofadder 270. Note that in the ideal case, the frequency at the output ofadder 270 should become equal to zero. An alternative approach is to measure the frequency difference and correct the estimated phase without adjusting the TCXO frequency of the transponder.

Although beta can be significantly reduced^M-β^AMBut when beta is^MPhase estimation errors still exist when not equal to 1. In this case, the margin of error depends on the long-term stability of the clock generator of the reference device (usually the master device (M)). Furthermore, the process of clock synchronization may require considerable time, especially when a large number of units are used in a field. During synchronization, the tracking location system becomes partially or completely disabledOperationally, this negatively impacts system readiness and performance. In this case, the above-described method of TCXO frequency adjustment of the repeater is preferably not required.

Commercially available (off-the-shelf) TCXO components have a high degree of accuracy and stability. In particular, TCXO components for GPS commercial applications are very accurate. With these devices, the phase error can affect the positioning accuracy by less than one meter without the need for frequent clock synchronization.

Obtaining returned narrow bandwidth ranging signal complex amplitudes at a narrow bandwidth ranging signal multipath mitigation processor

After that, further processing (i.e. performing the super resolution algorithm) is implemented in a software-based component that is part of the multipath mitigation processor. The software components may be implemented in the host computer CPU of the host device (reader) and/or a microprocessor embedded in FPGA 150 (not shown). In one embodiment, the multipath mitigation algorithm software component is executed by a host computer CPU of the host device.

Super-resolution algorithm generation pair (2 pi x tau)_K) "frequency" (e.g., τ)_KValue). At the final step, the multipath mitigation processor selects τ with the minimum value (i.e., DLOS delay time).

In some cases where the narrow bandwidth requirements of the ranging signal are somewhat relaxed, the DLOS path may be separated from the MP path by employing a continuous (in time) chirp. In one embodiment, the continuous chirp is a chirp modulation (LFM). However, other chirp waveforms may be used.

It is assumed that a chirp signal having a bandwidth of B and a duration of T is transmitted under the control of the multipath mitigation processor. This gives

Radian per second chirp rate. A plurality of chirp signals can be transmitted and received. It is noted that the chirp signal is generated digitally, with each chirp beingChirp starts at the same phase.

In a multipath processor, each received single chirp signal is aligned such that the returned chirp signal is from the middle of the region of interest.

The chirp waveform equation is:

s(t)＝exp(i(ω₀t+βt²) Where ω) is₀Is an initial frequency of 0 < T < T.

For a single delayed round trip τ, e.g., no multipath is present, the returned signal (chirp signal) is s (t- τ).

The multipath mitigation processor then "deskews" s (t- τ) by performing complex conjugate mixing with the initially transmitted chirp signal. The resulting signal is a complex sine wave:

f_τ(t)＝exp(-ω₀τ)exp(-2iβτt)exp(iβτ²)， (8)

Wherein exp (-iw)₀τ_k) Is the amplitude and 2 β τ is the frequency and 0 ≦ T ≦ T. Note that the last term is phase, and that the phase is negligible.

In the case of multipath, the composite deskewed signal is composed of a plurality of complex sinusoids:

where L is the number of ranging signal paths, including DLOS paths and 0 ≦ T ≦ T.

A plurality of chirp signals can be transmitted and processed. Each chirp signal is operated on/processed separately as described above. Thereafter, the multipath mitigation processor assembles the results of the individual chirp signal processing:

where N is the number of chirp signals,

ρ＝T+t_dead；t_deadis the dead time between two consecutive chirp signals; 2 beta tau_kIs an artificial delay "frequency". Also, the most interesting is the lowest "frequency" corresponding to the DLOS path delay.

In the case of the equation (10),

the sum of the complex sinusoids can sometimes be considered as N samples:

0≤t_α≤T；t₁＝t_α+ρ；t₂＝t_α+2ρ.....；t_m-1＝t_α+(N-1)ρ；m∈0:m-1；

thus, the number of samples may be a multiple of N, e.g., an; α is 1, 2.

According to equation (10), the multipath mitigation processor generates α N complex amplitude samples in the time domain for further processing (i.e., performing a super resolution algorithm). The further processing is implemented in a software component that is part of the multipath mitigation processor. The software components may be executed by the host computer CPU of the host device (reader) and/or by a microprocessor (not shown) embedded inFPGA 150, or by both. In one embodiment, the multipath mitigation algorithm software is executed by the host computer CPU of the host device.

Super-resolutionalgorithm generation pair 2 beta tau_k"frequency" (e.g., τ)_KValue). At the final step, the multipath mitigation processor selects τ with the minimum value (i.e., DLOS delay time).

A special processing method called "threshold technique" which can be used as an alternative to the super-resolution algorithm will be explained below. In other words, the method is used to enhance the reliability and accuracy of distinguishing DLOS paths from other MP paths using artificially generated synthetic wider bandwidth ranging signals.

The frequency domain baseband ranging signals shown in fig. 1 and 1A may be converted to a time domain baseband signal s (t):

it is easy to verify that s (t) is periodic withperiod 1/Δ t, and for any integer k, s (k/Δ t) ═ 2N +1, which is the peak of the signal. Wherein N ═ N in fig. 1 and 1A.

Fig. 4 shows two periods of s (t) for the case of N11 andΔ f 250 kHz. The signal appears as a sequence of pulses ofheight 2N +1 23 separated by 1/af-4 microseconds. Between the pulses are sinusoidal waveforms with varying amplitudes and 2N zero. The wide bandwidth of the signal is attributable to the narrowness of the high pulses. It can also be seen that the bandwidth extends from zero frequency to N Δ f 2.75 MHz.

The basic idea of the thresholding method used in one embodiment is to enhance the artificially generated synthetic wider bandwidth ranging reliability and accuracy when distinguishing DLOS paths from other MP paths. The thresholding method detects when the beginning of the leading edge of the wideband pulse reaches the receiver. Due to the filtering in the transmitter and receiver, the leading edge does not rise instantaneously but rises out of the noise with a smoothly increasing slope. The TOA of the leading edge is measured by detecting when the leading edge crosses a predetermined threshold T.

A smaller threshold is desirable because it crosses faster and the error delay tau between the true start of the pulse and the threshold crossing is smaller. Thus, if the start of the copy has a delay greater than τ, any pulse that arrives due to multipath has no effect. However, the presence of noise imposes a limit on how small the threshold T may be. One way to reduce the delay τ is to use the derivative of the received pulse rather than the pulse itself, since the derivative rises faster. The second derivative has an even faster rise. Higher order derivatives may be used, but in practice higher order derivatives may raise the noise level to an unacceptable value, so a threshold second order derivative is used.

Although the 2.75MHz wide signal shown in fig. 4 has a fairly wide bandwidth, it is not suitable for measuring the range by the above method. This method requires firing pulses each with a zero signal precursor. However, this goal can be achieved by modifying the signal such that the sinusoidal waveforms between the pulses are substantially cancelled. In one embodiment, the method is accomplished by constructing a waveform that closely approximates the signal over the selected interval between the high pulses, and then subtracting the signal from the original signal.

This technique can be illustrated by applying it to the signals in fig. 1. The two black dots shown on the waveform are the end points of the interval I between the first two pulses. The left and right endpoints of interval I, determined experimentally to provide the best results, are located respectively:

an attempt is made to generate a function g (t) which substantially cancels the signal s (t) over the interval, but does not have much influence outside the interval. Since expression (11) indicates that s (t) is a sine wave sin pi (2N +1) Δ ft modulated by 1/sin pi Δ ft, first find the function h (t) that closely approximates 1/sin pi Δ ft over interval I, and then form g (t) as the product:

g(t)＝h(t)sinπ(2N+1)Δft (13)

h (t) is generated by the sum of:

Wherein

φ₀(t)≡1，φ_k(t) ═ sink pi Δ ft for k ═ 1, 2,.., M (15)

And selecting the coefficient a_kTo minimize least squares error

Within interval I.

By obtaining J vs. a_kAnd set them equal to zero, the solution can be easily obtained. The result is thatLinear system of M +1 equation

This can solve for a_kWherein

Then, the user can use the device to perform the operation,

using the function phi given by (12)_kDefinition of (t)

Subtracting s (t) from g (t) to obtain a function r (t) that substantially cancels s (t) over the interval I. As shown in the appendix, for the summation in equation (20), a suitable choice of the upper limit M is M2N + 1. Using this value and the results of the appendix,

wherein

As can be seen from equation (17), a total of 2N +3 frequencies (including the zero frequency DC term) are required to obtain the desired signal r (t). Fig. 5 shows the resulting signal r (t) of the original signal s (t) shown in fig. 1, where N ═ 11. In this case, the construction of r (t) requires 25 carriers (including the DC term b)₀)。

Important characteristics of r (t) constructed as above are as follows:

1. from (14), it can be seen that the lowest frequency is zero hertz and the highest frequency is (2N +1) Δ fHz. Thus, the total bandwidth is (2N +1) Δ fHz.

2. All carriers are cosine functions (including DC) spaced by Δ f except that one of the carriers is at frequency

Is calculated as a sine function of (c).

3. While the initial signal s (t) has a period of 1/Δ f, r (t) has a period of 2/Δ f. The first half of each period of r (t), which is the complete period of s (t), contains the cancellation portion of the signal, and the second half of r (t) is the large oscillation segment. Therefore, the elimination of the precursor only occurs at every other cycle of s (t).

This is because the cancellation function g (t) actually enhances s (t) in every other cycle of s (t). The reason is that g (t) reverses its polarity at each peak of s (t), while s (t) does not. The following describes a method of including a portion of each period of s (t) that is eliminated to increase the processing gain by 3 dB.

The length of the offset portion of s (t) is about 80% to 90% of 1/Δ f. Therefore, Δ f needs to be small enough to make the length long enough to eliminate any residual signal from the previous non-zero portion of r (t) due to multipath.

5. The first cycle of the oscillation portion immediately follows each zero portion of r (t). In one embodiment, in the TOA measurement method described above, the first half of the cycle is used to measure the TOA, particularly when the TOA begins to rise. It is noted that the peak value of the first half period (which will be referred to as the main peak) is slightly larger than the corresponding peak value of s (t) at approximately the same point in time. The width of the first half period is approximately inversely proportional to N Δ f.

6. A large amount of processing gain can be achieved by:

(a) since r (t) is periodic withperiod 2/Δ f, the signal r (t) is reused. In addition, an additional processing gain of 3dB can be obtained by a method to be described later.

(b) And (4) narrow-band filtering. Since each of the 2N +3 carriers is a narrowband signal, the occupied bandwidth of the signal is much smaller than the occupied bandwidth of a wideband signal spread over the entire allocated frequency band.

For the signal r (t) shown in fig. 5, where N11 and Δ f 250kHz, the length of the cancellation portion of s (t) is about 3.7 microseconds or 1110 meters. This is sufficient to eliminate any residual signal from the previous non-zero portion of r (t) due to multipath. The main peak has a value of about 35, and the maximum magnitude in the precursor (i.e., elimination) region is about 0.02, 65dB below the main peak. This is desirable for good performance using the TOA measurement thresholding technique described above.

The use of fewer carriers is depicted in fig. 6, which shows signals generated using Δ f 850kHz, N3, and M2N +1 7, for a total of only 2N + 3-9 carriers. In this case, the signal period is only as long as compared to the signal in fig. 5

Microseconds, with a period of 8 microseconds. Since this example has more cycles per unit time, it is expected that more processing gains will be realized.

However, since fewer carriers are used, the amplitude of the main peak is approximately 1/3 f, which tends to offset the expected additional processing gain. In addition, the length of the zero signal precursor segment is short, about 0.8 microseconds or 240 meters. This should still be sufficient to eliminate any residual signal from the previous non-zero portion of r (t) due to multipath. Note that the total bandwidth of (2N +1) Δ f-5.95 MHz is approximately the same as before, and the width of the half period of the main peak is also approximately the same as before. Since fewer carriers are used, there should be some additional processing gain when narrowband filtering each carrier at the receiver. Furthermore, the maximum magnitude in the precursor (i.e., elimination) region is now about 75dB lower than the main peak, which is a 10dB improvement over the previous example.

Transmission at RF frequency: for simplicity, r (t) has been described so far as a baseband signal. However, it can be converted to RF, transmitted, received, and then reconstructed at the receiverIs a baseband signal. To illustrate, consider a frequency component ω in a baseband signal r (t) traveling via one of the multipath propagation paths with index j_kOne of the frequency components is changed (for simplicity, radian/second frequency is used):

It is assumed here that the transmitter and receiver are frequency synchronized. Parameter b_kIs the kth coefficient for r (t) in expression (21). Parameter tau_jAnd phi_jRespectively the path delay and the phase shift of the jth propagation path (due to the dielectric properties of the reflector). The parameter θ is the phase shift that occurs when down to baseband in the receiver. A similar sequence of functions may be presented for the sinusoidal components of equation (21).

It is important to note that the final baseband signal in equation (20) will still have zero signal precursors as long as the zero signal precursors in r (t) are sufficiently larger than the length of the maximum significant propagation delay. Of course, when all frequency components (index k) on all paths (index j) are combined, the baseband signal at the receiver will be a distorted version of r (t), including all phase shifts.

Fig. 1 and 1A show sequential carrier transmission and signal reconstruction. Assuming that the transmitter and receiver are time and frequency synchronized, there is no need to transmit the 2N +3 transmitted carriers simultaneously. For example, consider the transmission of signals whose baseband representation is that of fig. 1A and 6.

In fig. 6, N is 3, and it is assumed that each of 9 frequency components of 1 msec is sequentially transmitted. The start time and end time of each frequency transmission is known at the receiver, so the receiver can start and end reception of each frequency component sequentially at those respective times. Since the signal propagation time is very short compared to 1 millisecond (which in the intended application will typically be less than a few microseconds), a small portion of each received frequency component should be ignored and easily blanked by the receiver.

The entire process of receiving 9 frequency components may be repeated in additional 9 millisecond receive blocks to increase processing gain. Within one second of total receive time, there will be about 111 such 9 millisecond blocks available for processing gain. In addition, within each block, there will be a slave

Additional processing gain obtained by the main peak.

It is worth noting that signal reconstruction can be very economical in general and will inherently allow all possible processing gains. For each of the 2N +3 received frequencies:

1. the phase and amplitude of each 1 millisecond reception of the frequency is measured to form a sequence of stored vectors (phasors) corresponding to the frequency.

2. The stored vector of frequencies is averaged.

3. Finally, the 2N +3 vector average of the 2N +3 frequencies is used to reconstruct 1 cycle of the baseband signal withduration 2/Δ f, and this reconstruction is used to estimate the signal TOA.

The method is not limited to 1 millisecond transmissions and the length of the transmission may be increased or decreased. However, the total time of all transmissions should be short enough to freeze any response of the receiver or transmitter.

The following can be obtained over an alternative half period (t) of r: by simply reversing the polarity of the cancellation function g (t), cancellation between the peaks of s (t) is possible where r (t) was the previous oscillation. However, in order to obtain cancellation between all peaks of s (t), the function g (t) and its polarity-reversed version must be applied at the receiver, and this involves weighting of the coefficients at the receiver.

Coefficient weights at the receiver: coefficient b in equation (21), if necessary_kFor constructing r (t) at the transmitter, coefficients may also be introduced at the receiver. This can be easily seen by considering the signal sequence in equation (20), where b is introduced if it is in the last step, not at the beginning_kThe final signal is the same. Ignoring noise, the values are as follows:

the transmitter can then transmit all frequencies with the same amplitude, which simplifies the transmitter design. It should be noted that the method also weights the noise at each frequency, the influence of which should be taken into account. It should also be noted that the coefficient weighting should be done at the receiver in order to influence g (t) polarity inversion to get twice the available main peak.

Scaling Δ f to the channel center frequency: to meet the FCC requirements at VHF or lower frequencies, a channelized transmission with constant channel spacing would be required. In a channelized transmission band with a small constant channel spacing compared to the total allocated band (VHF and lower band case), small adjustments to Δ f allow all transmission frequencies to be at the channel center if needed without significantly changing performance from the original design values. In both examples of the previously presented baseband signal, all frequency components are multiples of Δ f/2, so if the channel spacing is divided by Δ f/2, the lowest RF transmission frequency can be centered in one channel and all other frequencies fall at the center of the channel.

In some Radio Frequency (RF) based identification, tracking and location systems, in addition to performing distance measurement functions, both the master unit and the tag unit perform voice, data and control communication functions. Similarly, in one embodiment, both the master unit and the tag unit perform voice, data and control communications functions in addition to the distance measurement function.

According to an embodiment, ranging signals are subject to a wide range of complex signal processing techniques, including multipath suppression. However, these techniques may not be suitable for voice, data, and control signals. Thus, the operating range of the proposed system (as well as other existing systems) may not be limited by its ability to reliably and accurately measure distance, but rather out of range during voice and/or data and/or control communications.

In other Radio Frequency (RF) based identification, tracking and location systems, the distance measurement function is separated from the voice, data and control communication functions. In these systems, separate RF transceivers are used to perform voice, data, and control communication functions. The disadvantage of this approach is that it increases the cost, complexity, size, etc. of the system.

To avoid the above disadvantages, in one embodiment, several individual frequency components of the narrow bandwidth ranging signal or the baseband narrow bandwidth ranging signal are modulated with the same data/control signal and modulated in the case of speech with digitized voice packet data. At the receiver, the individual frequency components with the highest signal strengths are demodulated, and the reliability of the obtained information can be further enhanced by performing "voting" or other signal processing techniques that exploit information redundancy.

This approach allows avoiding the "null" phenomenon, where the incoming RF signal from multiple paths and the DLOS path destructively combine with each other, significantly reducing the received signal strength and correlating with the SNR of the signal. Furthermore, this approach allows finding a set of frequencies at which the incoming signal from multiple paths and the DLOS path are closely coupled to each other, thereby increasing the received signal strength and correlating with the SNR of the signal.

As mentioned before, super-resolution algorithms based on spectral estimation typically use the same model: a linear combination of the complex exponential and its complex frequency amplitude. The complex amplitude is given byequation 3 above.

All super-resolution algorithms based on spectral estimation require a priori knowledge of the number of complex indices (i.e. the number of multipath paths). The number of complex exponentials is called the model size and is determined by the number of multipath components L, as shown inequations 1 to 3. However, this information is not available when estimating the path delay, which is the case for RF tracking location applications. This adds another dimension (i.e., model size estimation) to the spectrum estimation process through a super-resolution algorithm.

It has been shown (Kei Sakaguchi et al, information of the Model Order Estimation error in the ESPRIT Based High Resolution Techniques), that the accuracy of frequency Estimation is affected in case of underestimated Model size, and that the algorithm may generate spurious (e.g., non-existing) frequencies when Model size is overestimated. Existing model size estimation methods such as Akaikes Information Criterion (AIC), Minimum Description Length (MDL), etc. are highly sensitive to correlation (complex exponential) between signals. But in the case of RF multipath this is always the case. Even, for example, after applying a forward-backward smoothing algorithm, there will always be a residual amount of correlation.

In the Sakaguchi paper it is proposed to use an overestimation model and to distinguish actual frequencies (signals) from spurious frequencies (signals) by estimating the power (amplitude) of these signals and then rejecting them at very low power. Although this method is an improvement over the existing methods, it is not guaranteed. The inventors have implemented the method of Kei Sakaguchi et al and run simulations for more complex cases with larger model sizes. It is observed that in some cases, the spurious signals may have amplitudes that are very close to the actual signal amplitude.

All super-resolution algorithms based on spectral estimation work by separating the incoming signal complex amplitude data into two subspaces: noise subspace and signal subspace. If these subspaces are properly defined (separated), the model size is equal to the signal subspace size (magnitude).

In one embodiment, the model size estimation is done using "F" statistics. For example, for the ESPRIT algorithm, the odd-valued decomposition of the estimate of the covariance matrix (using the forward/backward correlation smoothing algorithm) is ordered in ascending order. Thereafter, a division is performed, whereby the (n +1) eigenvalue is divided by the nth eigenvalue. This ratio is an "F" random variable. The worst case is the "F" random variable for the (1,1) degree of freedom. The 95% confidence interval for the random variable of "F" for the (1,1) degree of freedom is 161. Setting this value as a threshold determines the model size. Note also that for the noise subspace, the eigenvalues represent estimates of the noise power.

This method of applying "F" statistics to the ratio of eigenvalues is a more accurate method of estimating the model dimensions. It should be noted that other degrees of freedom in the "F" statistics may also be used for threshold calculation and thus for model size estimation.

However, in some cases, two or more very closely spaced (in time) signals may degrade into one signal due to real-world measurement imperfections. Therefore, the above method will underestimate the number of signals, i.e. the model size. Increasing the model size by adding a certain number is prudent, since underestimating the model size reduces the frequency estimation accuracy. The amount can be determined experimentally and/or by simulation. However, when the spacing of the signals is not tight, the model size will be overestimated.

In such cases, spurious (i.e., non-existent) frequencies may occur. As previously mentioned, using signal amplitude for spurious signal detection is not always effective because in some cases, spurious signals are observed to have amplitudes that are very close to the actual signal amplitude. Thus, in addition to amplitude discrimination, a filter may be implemented to increase the spurious frequency cancellation probability.

The frequency estimated by the super-resolution algorithm is an artificial frequency (equation 2). In fact, these frequencies are individual path delays of a multi-path environment. Therefore, there should be no negative frequencies, and all negative frequencies generated by the super-resolution algorithm are pseudo frequencies to be rejected.

Further, the DLOS distance range may be obtained by using a method other than the super resolution method to obtain the complex amplitude during measurement

The value is estimated. Although these methods are less accurate, the method determines a range for distinguishing the delays (i.e., frequencies). For example, a ratio

In which the signal amplitude

In the Δ f interval, which is close to the maximum (i.e., avoids zero), a DLOS delay range is provided. Although the actual DLOS delay can be as much as two times more or less, this limits the range that helps reject spurious results.

In this embodiment, the ranging signal makes a round trip. In other words, the ranging signal travels in two ways: from master/reader to target/slave and from target/slave back to master/reader:

master device transmission tone: α × e^-jωtWhere ω is the operating frequency in the operating frequency band and α is the pitch signal amplitude.

At the receiver of the target device, the received signal (unidirectional) is as follows:

wherein: n is the number of signal paths in a multipath environment; k0 and τ₀Is the amplitude and time of flight of the DLOS signal; i K₀|＝1、K₀＞0、|K_m≠0Less than or equal to 1 and K_m≠0May be positive or negative.

S_One-way(t)＝α×e^-jωt×A(ω)×e^-jθ(ω) (26)

Wherein:

is a one-way multipath RF channel transfer function in the frequency domain; and A (ω) ≧ 0.

The target unit retransmits the received signal:

S_{retransmission}(t)＝α×e^-jωt×A(ω)×e^-jθ(ω) (27)

At the receiver of the master device, the round trip signal is:

or:

S_{to and from}(t)＝α×e^-jωt×A2(ω)×e^-j2θ(ω) (28)

On the other hand, according to equations (26) and (28):

wherein:

is the round-trip multipath RF channel transfer function in the frequency domain.

According to equation 29, a round-trip multipath channel has a greater number of paths than a unidirectional channel multipath because except for τ₀÷τ_NOut of path delay

The expression also includes combinations of these path delays, e.g. τ₀+τ₁、τ₀+τ₂……、τ₁+τ₂、τ₁+τ₃… …, etc.

These combinations significantly increase the number of signals (complex exponential). Thus, the probability of very closely spaced (in time) signals will also increase, and may result in significant model size underestimation. Therefore, it is desirable to obtain a one-way multipath RF channel transfer function.

In one embodiment, the unidirectional amplitude values

May be obtained directly from the target device/slave device. However, a one-way phase value

It cannot be measured directly. The one-way phase can be determined from round-trip phase measurement observations:

however, for each value of ω, there are two phase values α (ω) such that

e^j2α(ω)＝e^jβ(ω)

A detailed description of resolving this ambiguity is shown below. If the different frequency components of the ranging signal are close to each other, the one-way phase can be found for most part by dividing the round-trip phase by two. Exceptions will include regions near "zero" where the phase may undergo significant changes even at small frequency steps. Note that: the "null" phenomenon is one in which the incoming RF signal and DLOS path from multiple paths combine destructively with each other, significantly reducing the received signal strength and correlating with the SNR of the signal.

Let h (t) be the one-way impulse response of the communication channel. The corresponding transfer function in the frequency domain is:

where A (ω) ≧ 0 is the magnitude and α (ω) is the phase of the transfer function. If the one-way impulse response is retransmitted back through the same channel as it is being received, the resulting two-way transfer function is:

G(ω)＝B(ω)e^jβ(ω)＝H²(ω)＝A²(ω)e^j2α(ω) (31)

wherein B (omega) is not less than 0. Suppose that the two-way transfer function G (ω) is for some open frequency interval (ω)₁,ω₂) All ω in (a) are known. Whether it can be determined at (ω)₁,ω₂) The above-defined one-way transfer function H (ω)?

Since the magnitude of the two-way transfer function is the square of the one-way magnitude, it is clear that

However, the situation is more difficult to perceive when trying to recover the phase of the one-way transfer function from the G (ω) observation. For each value in ω, there isTwo areA phase value alpha (omega) such that

e^j2α(ω)＝e^jβ(ω) (33)

A number of different solutions may be generated by selecting one of the two possible phase values independently for each different frequency ω.

The following theory, which assumes that any one-way transfer function is continuous at all frequencies, helps to address this situation.

Theorem 1: if I is not a bidirectional transfer function G (ω) ═ B (ω) e^jβ(ω)Open interval of frequency ω of zero. Is provided with

Is a continuous function on I, where: β (ω) ═ 2 γ (ω). J (ω) and-J (ω) are one-way transfer functions that produce G (ω) on I, and no other function is present.

Certifying that: one of the solutions to the one-way transfer function is a function

Since the borrowing is distinguishable on I, it is continuous on I, and where β (ω) is 2 α (ω). Since G (ω) ≠ 0 at I, H (ω) and J (ω) are nonzero at I. Then, the user can use the device to perform the operation,

since H (ω) and J (ω) are continuous and non-zero in I, the ratio of the two is continuous in I, so the right side of (34) is continuous in I. The condition β (ω) ═ 2 α (ω) ═ 2 γ (ω) means that α (ω) - γ (ω) is 0 or pi for each ω ∈ I. However, α (ω) - γ (ω) cannot switch between these two values without causing a discontinuity on the right side of (34). Thus, for all ω ∈ I, α (ω) - γ (ω) ═ 0; or α (ω) - γ (ω) ═ pi for all ω ∈ I. In the first case, we get J (ω) ═ H (ω), and in the second case, we get J (ω) ═ H (ω).

This theorem proves that, in order not to include the transfer function G (ω) ═ B (ω) e^jβ(ω)Obtain a one-way solution over any open interval I of zeros, we form a function

The value of γ (ω) satisfying β (ω) ═ 2 γ (ω) is selected so that J (ω) continues. This approach can always be employed since it is known that there is a solution with this property, i.e., H (ω).

An alternative process for finding a one-way solution is based on the following theory:

theorem 2: let H (ω) be A (ω) e^jα(ω)Is a one-way transfer function, and let I be the open frequency of frequency ω of zeros that do not contain H (ω). The phase function α (ω) of H (ω) must be continuous over I.

Certifying that: let omega₀Is the frequency in interval I. In FIG. 7, the complex value H (ω)₀) Has been plotted as a point in the complex plane and by assuming, H (ω)₀) Not equal to 0. Let ε > 0 be an arbitrarily small real number, and consider the two measurement angles ε shown in FIG. 7, and consider the two angles ε as H (ω)₀) A circle centered and tangent to the two rays OA and OB. H (ω) is assumed to be continuous for all ω. Thus, if ω is close enough to ω₀The complex value H (ω) will lie in a circle and | α (ω) - α (ω) can be seen₀) And | < ε. Since ε > 0 was chosen arbitrarily, we conclude that: α (ω) → α (ω)₀) As ω → ω₀So that the phase function α (ω) is at ω₀Is continuous.

Theorem 3: if I is not a bidirectional transfer function G (ω) ═ B (ω) e^jβ(ω)Open interval of frequency ω of zero. Is provided with

Is a function on I, where β (ω) ═ 2 γ (ω) and γ(ω) is continuous over I. J (ω) and-J (ω) are one-way transfer functions that produce G (ω) on I, and no other function is present.

Certifying that: this proof is similar to that oftheorem 1. It is known that one of the solutions of the one-way transfer function is a function

Wherein β (ω) ═ 2 α (ω). Since G (ω) ≠ 0 at I, H (ω) and J (ω) are nonzero at I. Then, the user can use the device to perform the operation,

by assuming that γ (ω) is continuous over I, andtheorem 2 α (ω) is also continuous over I. Thus, α (ω) - γ (ω) is continuous over I. The condition β (ω) ═ 2 α (ω) ═ 2 γ (ω) means that α (ω) - γ (ω) is 0 or pi for each ω ∈ I. However, α (ω) - γ (ω) cannot switch between these two values without becoming discontinuous over I. Thus, for all ω ∈ I, α (ω) - γ (ω) ═ 0; or α (ω) - γ (ω) ═ pi for all ω ∈ I. In the first case, we obtain J (ω) ═ H (ω), and in the second case, J (ω) ═ H (ω).

Fromtheorem 3, it can be seen that in order not to include the transfer function G (ω) ═ B (ω) e^jβ(ω)Obtain a one-way solution over any open interval I of zeros, we form a function

The value of γ (ω) satisfying β (ω) ═ 2 γ (ω) is selected so that the phase function γ (ω) continues. This approach can always be employed since it is known that there is a solution with this property, i.e., H (ω).

Although the above theorem indicates how to reconstruct the two unidirectional transfer functions that generate the bidirectional function G (ω), they are only useful over a frequency interval I that does not contain zeros of G (ω). Generally, G (ω) will be at a frequency interval (ω) that may contain zero₁,ω₂) As observed above. The following are ways in which this problem can be circumventedFa, suppose in (ω)₁,ω₂) Has only a limited number of zeros of G (ω) and the one-way transfer function is at (ω)₁,ω₂) Up, and not all order derivatives are zero at any given frequency ω:

let H (ω) be at interval (ω)₁,ω₂) Generates a one-way function of G (ω) and assumes that G (ω) is at (ω)₁,ω₂) Has at least one zero. Zero of G (omega) will be (omega)₁,ω₂) Divided into a finite number of contiguous open frequency intervals J₁,J₂,...,J_n. Within each such interval,theorem 1 ortheorem 3 is used to find the solution H (ω) or-H (ω). We need to "stitch together" these solutions so that the stitched solution is (ω)₁,ω₂) H (ω) or-H (ω) on all of (a). To achieve this, we need to know how to unpair in two adjacent subintervals, so that we do not switch from H (ω) to-H (ω) or from-H (ω) to H (ω) in moving from one subinterval to the next.

We show the first two adjacent open subintervals J₁And J₂The suturing process is initiated. These subintervals will be at a frequency ω of zero for G (ω)₁Adjacent (of course,. omega.)₁Not included in any subinterval). With our above assumptions about the properties of the one-way transfer function, there must be a minimum positive integer n, such that where H is⁽ⁿ⁾(ω₁) Not equal to 0, where superscript (n) denotes nth order derivative. Then, according to our at J₁Whether the solution in (d) is H (ω) or-H (ω), we are at J₁Middle as ω → ω₁Will be H from the limit on the left of the nth derivative of the unidirectional solution⁽ⁿ⁾(ω₁) or-H⁽ⁿ⁾(ω₁). Similarly, according to our at J₂Whether the solution in (d) is H (ω) or-H (ω), we are at J₂Middle as ω → ω₁Will be H from the limit on the right side of the nth order derivative of the unidirectional solution⁽ⁿ⁾(ω₁) or-H⁽ⁿ⁾(ω₁). Due to H⁽ⁿ⁾(ω₁) Not equal to 0, so if and only if J₁And J₂The solution in (C) is both H (ω)or-H (ω), then these two limits will be equal. If the left and right limits are not equal, we are in subinterval J₂The middle inversion solution. Otherwise, we do not.

In the sub-interval J₂After the middle inversion solution (if needed), we are for the subinterval J₂And J₃The same procedure is executed, thereby in the subinterval J₃The middle inversion solution (if needed). Continuing in this manner, we end up in this interval (ω)₁,ω₂) A complete solution is built in.

It is desirable that the higher order derivatives of H (ω) are not required in the reconstruction process described above because it is difficult to accurately calculate these higher order derivatives in the presence of noise. This problem is unlikely to occur because at any zero of G (ω), it seems likely that the first derivative of H (ω) will be non-zero, and if not, it is likely that its second derivative will be non-zero.

In a practical scenario, the two-way transfer function G (ω) will be measured at discrete frequencies that must be close enough together to enable reasonably accurate calculation of the derivative near zero of G (ω).

For RF-based distance measurement, an unknown number of closely spaced, overlapping and noisy echoes of ranging signals with a priori known shapes must be addressed. Assuming that the ranging signal is narrowband, in the frequency domain, the RF phenomenon can be described (modeled) as a sum of the number of sinusoids, each according to a multipath component and each having a complex attenuation and propagation delay of that path.

Fourier transforming the sum will represent the multipath model in the time domain. In exchange for the effects of time and frequency variations in the time domain representation, the multipath model will become a harmonic signal spectrum in which the propagation delays of the paths are converted to harmonic signals.

Ultra (high) resolution spectral estimation methods are designed to distinguish closely placed frequencies in the spectrum and to estimate individual frequencies, e.g. path delays, of a multi-harmonic signal. Therefore, the path delay can be accurately estimated.

The super-resolution spectral estimation utilizes the eigenstructure of the covariance matrix of the baseband ranging signal samples and the inherent properties of the covariance matrix to provide a solution to the underlying estimate for each frequency (e.g., path delay). One of the eigenstructure properties is that the eigenvalues can be combined and thus separated into orthogonal noise and signal eigenvectors (also called subspaces). Another intrinsic structural property is the rotation invariant signal subspace property.

Subspace decomposition techniques (MUSIC, rootMUSIC, ESPRIT, etc.) rely on splitting the estimated covariance matrix of the observed data into two orthogonal subspaces, namely the noise subspace and the signal subspace. The theory of the subspace decomposition method is that the projection of the observable on the noise subspace contains only noise, and the projection of the observable on the signal subspace contains only signal.

The spectral estimation method assumes that the signal is narrowband and the number of harmonic signals is also known, i.e. the size of the signal subspace needs to be known. The size of the signal subspace is referred to as the model size. Generally, any details of the model dimensions are not known and can change rapidly with changes in the environment (especially indoors). One of the most difficult and difficult to perceive problems when applying any subspace decomposition algorithm is the dimension of the signal subspace, which can be considered as the number of frequency components present and which is the number of multipath reflections plus direct paths. Due to real-world measurement imperfections, there will always be errors in the model size estimation, which in turn will cause a loss in the accuracy of the frequency estimation (i.e., distance).

To improve the distance measurement accuracy, one embodiment includes advancing the state of the art six features in a subspace decomposition high resolution estimation method. The invention includes combining two or more algorithms by using different eigen-structure properties that further reduce delay path determination ambiguity.

Root Music finds the individual frequencies, minimizing the projected energy when an observable is projected onto the noise subspace. The Esprit algorithm determines the individual frequencies from the rotation operator. And in many aspects, the operation is a conjugate of Music, as it finds the frequency that maximizes the energy projected when an observable is projected onto the signal subspace.

The model size is critical to both algorithms, and in practice, in complex signal environments (such as seen in indoor ranging), the model sizes that provide the best performance of Music and Esprit are typically not equal, for reasons that will be discussed below.

For Music, it is preferable that an error occurs on the side where the decomposed basic elements are recognized as "signal eigenvalues" (type I errors). This will minimize the amount of signal energy projected on the noise subspace and improve accuracy. For Esprit, it is preferable that an error occurs on the side where the basic element of the decomposition is recognized as the "noise eigenvalue" contrary to Music. This is also a type I error. This will minimize the effect of noise on the energy projected onto the signal subspace. Therefore, the model size of Music will typically be slightly larger than the model size of Esprit.

Secondly, in a complex signal environment, the following occurs: it is difficult to estimate the model size with sufficient statistical reliability due to the possibility that strong reflections and direct paths are actually much weaker than some of the multipath reflections. This problem is solved by estimating the "base" model sizes of Music and Esprit and processing the observable data using Music and Esprit in a model size window defined by the base model sizes of each model. This results in multiple measurements per measurement.

A first feature of this embodiment is the use of F-statistics to estimate model size (see above). The second feature is to use different type I error probabilities in the F statistics of Music and Esprit. This achieves a type I error difference between Music and Esprit, as described above. A third feature is the use of the basic model size and window in order to maximize the probability of detecting a direct path.

Not every measurement will provide a stable measurement due to the potentially rapidly changing physical and electronic environment. This is solved by using cluster analysis on multiple measurements to provide a robust range estimate. A fourth feature of this embodiment is the use of multiple measurements.

Because there are multiple signals, the probability distribution of multiple measurements from multiple measurements, each using multiple model sizes from Music and Esprit implementations, will be multi-modal. Conventional cluster analysis will not be sufficient for this application. A fifth feature is to develop multi-modal clustering analysis to estimate the direct range and equivalent range of the reflected multipath components. A sixth feature is the analysis of the statistics (range and standard deviation) of the range estimates provided by the cluster analysis and the combing of those statistically identical estimates. This allows for more accurate range estimation.

The method can also be used in a wide bandwidth ranging signal positioning system.

For the derivation of r (t) in the thresholding method, we obtain, starting from expression (20)

In which trigonometric identities are used

Except that a₀For even k, coefficient a_kIs zero. The reason for this is that over interval I we try thefunction 1/sin π Δ ft approximated with h (t) to be even with respect to the center of I, but for even k, the base function sink π Δ ft where k ≠ 0 is odd with respect to the center of I, and thus orthogonal to 1/sin π Δ ft on I. Therefore, we can replace k 2n +1 and let M be an odd positive integer. In fact, we will assume that M is 2N + 1. This option has been determined experimentally to eliminate a large number of oscillations in interval I.

Now we replace k N-N in the first summation and k N +1 in the second summation to obtain

Subtracting g (t) from the results of s (t)

Now is provided with

Then (A4) can be written as

The present embodiments are directed to a position/location method in wireless communications and other wireless networks that substantially obviates one or more of the disadvantages of the related art. The present embodiment advantageously improves the accuracy of tracking and positioning functions in many types of wireless networks by utilizing the multipath mitigation procedures, techniques and algorithms described in U.S. patent No. 7872583. These wireless networks include wireless personal area networks (WPGANs) such as ZigBee and bluetooth, Wireless Local Area Networks (WLANs) such as WiFi and UWB, Wireless Metropolitan Area Networks (WMANs) (typically consisting of a number of WLANs, WiMax being the primary example), wireless Wide Area Networks (WANs) such as white space television bands, and Mobile Device Networks (MDNs) typically used for transmitting voice and data. MDN is typically based on the global system for mobile communications (GSM) and Personal Communication Services (PCS) standards. The latest MDN is based on the Long Term Evolution (LTE) standard. These wireless networks are typically made up of a combination of the following devices: including base stations, desktop, tablet and laptop computers, handheld terminals, smart phones, actuators, application specific tags, sensors, and other communication and data devices (all of which are generally referred to as "wireless network devices").

Existing location and positioning information solutions use a variety of technologies and networks, including GPS, AGPS, mobile phone tower triangulation, and Wi-Fi. Some of the methods for deriving this location information include RF fingerprinting, RSSI, and TDOA. While acceptable for current E911 requirements, existing location and ranging methods do not have the reliability and accuracy required to support the upcoming E911 requirements and LBS and/or RTLS application requirements, especially in indoor and urban environments.

The method described in U.S. patent No. 7872583 significantly improves the ability to accurately locate and track a target device within a single wireless network or a combination of multiple wireless networks. This embodiment is a significant improvement over the existing implementations of tracking and location methods used by wireless networks using enhanced cell IDs and observed time difference of arrival (OTDOA), including downlink OTDOA (DL-OTDOA), U-TDOA, UL-TDOA, and other TDOAs.

Cell ID location techniques allow the location of a user (UE-user equipment) to be estimated with the accuracy of a particular sector coverage area. The achievable accuracy is therefore dependent on the cell (base station) sector scheme and the antenna beam width. To improve accuracy, the enhanced cell ID technique increases RTT (round trip time) measurements from the eNB. Note that: here, the RTT constitutes a difference between transmission of a downlink dedicated physical channel-dpch (dpdch)/dedicated physical data channel/Dedicated Physical Control Channel (DPCCH) frame and a start of a corresponding uplink physical frame. In this case, the above-mentioned frame serves as a ranging signal. Based on the information of the time it takes for the signal to propagate from the eNB to the UE, the distance to the eNB may be calculated (see fig. 10).

In the observed time difference of arrival (OTDOA) technique, the time of arrival of a signal from a neighboring base station (eNB) is calculated. Upon receiving signals from the three base stations, the location of the UE can be estimated in the handset (UE-based approach) or in the network (NT-based, UE-assisted approach). The measured signal is the common pilot channel (CPICH). The propagation time of the signal is correlated to the locally generated replica. The correlation peak indicates the observed propagation time of the measured signal. The difference in arrival times between the two base stations determines a hyperbola. At least three reference points are required to define two hyperbolas. The location of the UE is located at the intersection of these two hyperbolas (see fig. 11).

Downlink Idle Period (IPDL) is a further OTDOA enhancement. The OTDOA-IPDL technique is based on the same measurements as conventional OTDOA time measurements during idle periods, where the serving eNB stops its transmission and allows UEs in the coverage of the cell to listen to pilots from distant enbs. The serving eNB provides idle periods in a continuous or burst mode. In the continuous mode, one idle period is inserted in each downlink physical frame (10 ms). In burst mode, idle periods occur in a pseudo-random manner. Further improvements are obtained via temporal joint IPDL (TA-IPDL). The time union creates a common idle period during which each base station will stop its transmission to transmit the common pilot. Pilot signal measurements will occur during idle periods. There are several other techniques that may further enhance the DL OTDOA-IPDL method, such as cumulative virtual blanking, uplink tdoa (utdoa), etc. All of these techniques improve the ability to listen to other (non-serving) enbs.

One significant drawback of OTDOA-based techniques is that the base station timing relationship must be known or measured (synchronized) in order for the method to be feasible. For unsynchronized UMTS networks, the 3GPP standard will provide suggestions on how to recover this timing. However, network operators do not implement such solutions. Accordingly, an alternative scheme has been proposed to use RTT measurements instead of CPICH signal measurements (see U.S. patent publication No. 20080285505 to John Carlson et al entitled "SYSTEM AND METHOD FOR communicating reception IN COMMUNICATIONS NETWORKs".

All of the above methods/techniques are based on terrestrial signal time of arrival and/or time difference of arrival measurements (RTT, CPICH, etc.). A problem with such measurements is that they are severely affected by multipath. This in turn significantly reduces the Positioning/tracking accuracy of the above-described Method/technique (see "Performance of Cell ID + RTT Hybrid Positioning Method for UMTS" by Jakub Markek Borkowski).

One multipath mitigation technique uses detection/measurements from an excessive number of enbs or Radio Base Stations (RBSs). The minimum is three, but FOR multipath mitigation the number OF required RBSs is at least six to eight (see patent publication WO/2010/104436 entitled "METHOD AND ARRANGEMENT FOR DL-OTDOA (down on board TIME DIFFERENCE OF arroval) POSITIONING IN a LTE (LONG TERM EVOLUTION) WIRELESS COMMUNICATIONS SYSTEM"). However, the probability of the UE listening from this large number of enbs is much lower than the probability of listening from three enbs. This is because in case of a large number of RBSs (enbs), there will be several RBSs (enbs) far away from the UE, and the received signals from these RBSs may fall below the UE receiver sensitivity level, or the received signals will have a low SNR.

In the case of RF reflections (e.g., multipath), multiple copies of the RF signal with various delay times are superimposed onto the direct line of sight (DLOS) signal. Since CPICH, uplink DPCCH/DPDCH and other signals used in various cell ID and OTDOA methods/techniques (including RTT measurements) have limited bandwidth, DLOS signals and reflected signals cannot be distinguished without proper multipath processing/suppression; and without this multipath processing, these reflected signals would cause errors in estimated time difference of arrival (TDOA) and time of arrival (TOA) measurements, including RTT measurements.

For example, the 3G TS 25.515v.3.0.0(199-10) standard defines RTT as the "difference between the transmission of … … downlink DPCH frames (signals) and the reception of the beginning of the corresponding uplink DPCCH/DPDCH frames (signals) (first active path) from the UE". The standard does not define what constitutes the "first active path". The standard proceeds with the mention of "the definition of the first active path needs to be specified in further detail". For example, in a heavy multipath environment, it is common forAs a first effectRoute of travelIs severely attenuated (10dB to 20dB) relative to one or more reflected signals. If the "first active path" is determined by measuring the signal strength, the first active path may be one of the reflected signals rather than the DLOS signal. This will result in erroneous TOA/DTOA/RTT measurements and a loss of positioning accuracy.

In previous wireless network generation, positioning accuracy was also affected by the low sampling rate of frames (signals) used by the positioning methods-RTT, CPCIH, and other signals. Current third generation wireless networks and subsequent generations of wireless networks have much higher sampling rates. Thus, in these networks, the real impact of positioning accuracy comes from the terrestrial RF propagation phenomenon (multipath).

This embodiment may be used in all wireless networks that employ reference signals and/or pilot signals and/or synchronization signals, including single-duplex, half-duplex and full-duplex modes of operation. For example, this embodiment operates with wireless networks that employ OFDM modulation and/or derivatives thereof. Thus, this embodiment operates with an LTE network.

This embodiment is also applicable to other wireless networks including WiMax, WiFi, and white space. Other wireless networks that do not use reference signals and/or pilot signals or synchronization signals may employ one or more of the following types of alternative modulation embodiments as described in U.S. patent No. 7872583: 1) wherein a portion of the frame is dedicated to ranging signals/ranging signal elements, as described in U.S. patent No. 7872583; 2) wherein ranging signal elements (us patent No. 7872583) are embedded in the transmission/reception signal frames; and 3) in which ranging signal elements (described in U.S. patent No. 7872583) are embedded with data.

These alternative embodiments employ the multipath mitigation processor and multipath mitigation techniques/algorithms described in U.S. patent No. 7872583, and are applicable to all modes of operation: simple, half-duplex, and full-duplex.

It is also possible that multiple wireless networks will utilize the preferred and/or alternative embodiments simultaneously. By way of example, a smartphone may have bluetooth, WiFi, GSM, and LTE capabilities with the capability to operate on multiple networks simultaneously. Different wireless networks may be utilized to provide location/position information depending on application requirements and/or network availability.

The proposed embodiment methods and systems utilize wireless network reference/pilot signals and/or synchronization signals. Furthermore, reference signal/pilot signal/synchronization signal measurements may be combined with RTT (round trip time) measurements or system timing. Depending on the embodiment, RF-based tracking and positioning is implemented on a 3GPP LTE cellular network, but may also be implemented on other wireless networks employing various signaling techniques, such as WiMax, Wi-Fi, LTE, sensor networks, and the like. Both the exemplary embodiment and the above-described alternative embodiments employ the multipath mitigation methods/techniques and algorithms described in U.S. patent No. 7872583. The proposed system may use software implemented digital signal processing.

The system of this embodiment utilizes User Equipment (UE), such as a mobile phone or smart phone, hardware/software, and base station (node B)/enhanced base station (eNB) hardware/software. A base station typically consists of a transmitter and receiver in a cabin or cabinet connected to an antenna by a feeder. These base stations include microcells, picocells, macrocells, Umbrella cells, mobile phone towers, routing base stations, and femtocells. Thus, the UE device and the overall system will have little or no incremental cost. Meanwhile, the positioning accuracy is remarkably improved.

The improved accuracy comes from the multipath suppression provided by this embodiment and U.S. patent No. 7872583. Embodiments use multipath mitigation algorithms, network/pilot signals and/or synchronization signals and network nodes (enbs). These may supplement RTT (round trip time) measurements. The multipath mitigation algorithm is implemented in the UE and/or the base station (eNB) or both the UE and the eNB.

Embodiments advantageously use a multipath mitigation processor/algorithm (see U.S. patent No. 7872583) that allows for separation of DLOS signals and reflected signals even when the DLOS signals are significantly attenuated (10 dB to 20dB lower) relative to one or more reflected signals. Thus, this embodiment significantly reduces the error in the estimated time of flight of the ranging signal DLOS, and thus reduces TOA, RTT and DTOA measurements. The proposed multipath mitigation and DLOS differentiation (identification) method can be used for all RF bands and wireless systems/networks. And the multipath mitigation and DLOS differentiation (identification) method can support various modulation/demodulation techniques including spread spectrum techniques such as Direct Spread Spectrum (DSS) and Frequency Hopping (FH).

In addition, noise reduction methods may be applied in order to further improve the accuracy of the method. These noise reduction methods may include, but are not limited to, coherent summing, non-coherent summing, matched filtering, time diversity techniques, and the like. The residuals of multipath interference errors may be further reduced by applying post-processing techniques such as maximum likelihood estimation (e.g., viterbi algorithm), minimum variance estimation (kalman filter), and the like.

In this embodiment, the multipath mitigation processor and multipath mitigation techniques/algorithms do not change RTT, CPCIH, and other signals and/or frames. The present embodiments utilize wireless network reference, pilot, and/or synchronization signals for obtaining channel responses/estimates. The present invention uses channel estimation statistics generated by the UE and/or eNB (see US patents US 2003/008156, US 7167456B2 to Iwamatsu et al, entitled "APPARATUS FOR ESTIMATING pro pagation PATH CHARACTERISTICS").

LTE networks use specific (non-data) reference/pilot and/or synchronization signals (known signals) transmitted in each downlink and uplink subframe and may span the entire cell bandwidth. For simplicity, we now refer to the reference/pilot and synchronization signals as reference signals. An example of LTE reference signals is fig. 9 (these signals are interspersed between LTE resource elements). According to fig. 9, reference signals (symbols) are transmitted every six subcarriers. In addition, the reference signals (symbols) are staggered in time and frequency. In summary, the reference signal covers every third subcarrier.

These reference signals are used for initial cell search, downlink signal strength measurement, scheduling, handover, and the like of the UE. The reference signals include UE-specific reference signals for channel estimation (response determination) for coherent demodulation. In addition to UE-specific reference signals, other reference signals may also be used for channel estimation purposes (see Chen et al, U.S. patent publication 2010/0091826 a 1).

LTE employs Orthogonal Frequency Division Multiplexing (OFDM) modulation (technique). In LTE, inter-symbol interference (ISI) caused by multipath is handled by inserting a Cyclic Prefix (CP) at the beginning of each OFDM symbol. The CP provides sufficient delay so that the delayed reflection signal of the previous OFDM symbol will disappear before reaching the next OFDM symbol.

An OFDM symbol comprises a plurality of very closely spaced subcarriers. Within an OFDM symbol, a time-interleaved copy of the current symbol (caused by multipath) causes inter-carrier interference (ICI). In LTE, ICI is processed (suppressed) by determining the multipath channel response and correcting the channel response in the receiver.

In LTE, the multipath channel response (estimate) is calculated in the receiver from the subcarriers carrying the reference symbols. Interpolation is used to estimate the channel response on the remaining subcarriers. The channel response is calculated (estimated) in the form of channel amplitude and phase. Once the channel response is determined (by periodic transmission of a known reference signal), the channel distortion caused by multipath is mitigated by applying amplitude and phase shifts on a subcarrier-by-subcarrier basis (see Jim Zyren, overview of the 3GPP long term evolution physical layer, white paper).

LTE multipath mitigation is designed to remove both ISI (by inserting a cyclic prefix) and ICI, but not to separate the DLOS signal from the reflected signal. For example, the time-interleaved copies of the current symbol spread each modulated subcarrier signal in time, resulting in ICI. Correcting the multipath channel response using the above-described LTE techniques will reduce the modulated subcarrier signal in time, but this type of correction cannot guarantee that the resulting modulated subcarrier signal (within an OFDM symbol) is a DLOS signal. If the DLOS modulated subcarrier signal is significantly attenuated relative to the delayed reflected signal, the resulting output signal will be a delayed reflected signal and the DLOS signal will be lost.

In an LTE compatible receiver, further signal processing includes a Digital Fourier Transform (DFT). As is well known, DFT techniques may only resolve (remove) copies of a signal for a delay time that is longer than or equal to a time that is inversely proportional to the signal and/or channel bandwidth. The accuracy of this method may be sufficient for efficient data transmission, but not accurate enough to make accurate range measurements in a heavy multipath environment. For example, to achieve thirty meters accuracy, the signal and receiver channel bandwidths should be greater than or equal to ten megahertz (1/10MHz — 100 ns). To be more accurate, the signal and receiver channels should be wider for a wavelength bandwidth of three meters, i.e., one hundred megahertz.

However, CPICH, uplink DPCCH/DPDCH and other signals used in various cell ID and OTDOA methods/techniques (including RTT measurements) and LTE received signal sub-carriers have bandwidths significantly below ten megahertz. Thus, the methods/techniques currently employed (in LTE) will produce positioning errors in the range of 100 meters.

To overcome the above limitations, the present embodiment uses a unique combination of a subspace decomposition high resolution spectral estimation method and a specific implementation of multi-modal clustering analysis. This analysis and the associated multipath mitigation methods/techniques and algorithms described in U.S. patent No. 7872583 allow for reliable and accurate separation of DLOS paths from other reflected signal paths.

Compared to the methods/techniques used in LTE, in a heavy multipath environment, the method/techniques and algorithm (us patent No. 7872583) improves accuracy in distance measurements by a factor of 20 to 50 via reliable and accurate separation of DLOS paths from other Multipath (MP) paths.

The method/technique and algorithm described in us patent No. 7872583 requires a ranging signal complex amplitude estimate. Thus, LTE reference signals used for channel estimation (response determination) as well as other reference signals (including pilot and/or synchronization signals) may also be interpreted as ranging signals in the methods/techniques and algorithms described in U.S. patent No. 7872583. In this case, the ranging signal complex amplitude is the channel response calculated (estimated) by the LTE receiver in the form of amplitude and phase. In other words, the channel response statistics calculated (estimated) by the LTE receiver may provide the complex amplitude information required by the methods/techniques and algorithms described in U.S. patent No. 7872583.

In an ideal open space RF propagation environment without multipath, the phase (e.g., channel response phase) variation of the received signal (ranging signal) will be proportional to the frequency (line) of the signal; and the RF signal time of flight (propagation delay) in such an environment can be calculated directly from the phase versus frequency dependence by calculating the first derivative of the phase versus frequency dependence. The result will be a propagation delay constant.

In this ideal environment, the absolute phase value at the initial (or any) frequency is not important, as the derivative is not affected by the absolute value of the phase.

In a heavy multipath environment, the received signal phase variation is a complex curve (rather than a straight line) with respect to frequency; and the first derivative does not provide information that can be used for accurate separation of the DLOS path from other reflected signal paths. This is why the multipath mitigation processor and method/technique and algorithm described in us patent No. 7872583 are employed.

If the phase and frequency synchronization (phase coherence) achieved in a given wireless network/system is very good, the multipath mitigation processor and method/technique and algorithm described in U.S. patent No. 7872583 will accurately separate the DLOS path from the other reflected signal paths and determine the DLOS path length (time of flight).

In this phase coherent network/system, no additional measurements are required. In other words, one-way ranging (simple ranging) can be realized.

However, if the degree of synchronization (phase coherence) achieved in a given wireless network/system is not accurate enough, measurements of received signal phase and amplitude variations at two or more different locations (distances) with respect to frequency may be very similar in a heavy multipath environment. This phenomenon may lead to ambiguity in the determination of the distance (time of flight) of the received signal DLOS.

To resolve this ambiguity, the actual (absolute) phase value of at least one frequency must be known.

However, the amplitude and phase with respect to frequency dependence calculated by the LTE receiver do not include actual phase values, since all amplitude and phase values are calculated from the downlink/uplink reference signals, e.g., with respect to each other. Therefore, the amplitude and phase of the channel response calculated (estimated) by the LTE receiver require actual phase values at least one frequency (subcarrier frequency).

In LTE, the actual phase value may be determined by one or more RTT measurements, TOA measurements; or

Starting from the timestamps of one or more received reference signals, the provisos are 1) that these timestamps of these signals are also known at the receiver by the eNB (or vice versa), 2) that the receiver clock and the eNB clock are well synchronized in time, and/or 3) by using a multilateration technique.

All of the above methods provide time-of-flight values for one or more reference signals. From the time-of-flight values and frequencies of these reference signals, the actual phase values at one or more frequencies can be calculated.

The present embodiment achieves highly accurate DLOS distance determination/location in a heavy multipath environment by combining the multipath mitigation processors, methods/techniques and algorithms described in U.S. patent No. 7872583 with the following: 1) amplitude and phase with respect to frequency calculated by the LTE UE and/or eNB receiver or 2) amplitude and phase with respect to frequency dependency calculated by the LTE UE and/or eNB receiver in combination with actual phase values for one or more frequencies obtained via RTT and/or TOA; and/or time stamp measurements.

In these cases, the actual phase values are affected by multipath. However, this does not affect the performance of the methods/techniques and algorithms described in U.S. patent No. 7872583.

In LTE RTT/TOA/TDOA/OTDOA (including DL-OTDOA, U-TDOA, UL-TDOA, etc.), measurements may be performed at a resolution of 5 meters. RTT measurements are made during dedicated connections. Thus, multiple simultaneous measurements are possible when the UE is in a handover state, and when the UE periodically collects and reports measurements back to the UE, DPCH frames are exchanged between the UE and different networks (base stations). Similar to RTT, TOA measurements provide time of flight (propagation delay) of signals, but TOA measurements cannot be made simultaneously (see "performance of cell ID + RTT hybrid positioning method for UMTS" by Jakub mark Borkowski).

To locate a UE on a plane, distances/DLOS distances to at least three enbs must be determined. To locate a UE in three-dimensional space, the minimum four DLOS distances to/from four enbs must be determined (assuming that at least one eNB is not on the same plane).

An example of a UE positioning method is shown in fig. 1.

In case the synchronization is very good, no RTT measurement is needed.

If the degree of synchronization is not accurate enough, methods such as OTDOA, cell ID + RTT, etc., e.g., angle of arrival (AOA), and combinations thereof with other methods, may be used for UE positioning.

The accuracy of the cell ID + RTT tracking positioning method is affected by multipath (RTT measurement) and eNB (base station) antenna beam width. The base station antenna beamwidth is between 33 and 65 degrees. These wide beam widths result in positioning errors of 50 to 150 meters in urban areas (see "performance of cell ID + RTT hybrid positioning method for UMTS" by Jakub mark Borkowski). Considering that in a heavy multipath environment, the average error of the current LTE RTT distance measurement is about 100 meters, the overall expected average positioning error currently adopted by the LTE cell ID + RTT method is about 150 meters.

One of the embodiments is UE positioning based on AOA methods, whereby one or more reference signals from the UE are used for UE positioning purposes. This embodiment relates to an AOA determining device location for determining DLOS AOA. The device may be collocated with the base station and/or installed at another location or locations independent of the location of the base station. The coordinates of these locations are approximately known. The UE side does not need to change.

The device includes a small antenna array and is based on a variation of the same multipath mitigation processor, method/technique and algorithm described in U.S. patent No. 7872583. This one possible embodiment has the advantage of accurately determining (very narrow beamwidth) the AOA of DLOS RF energy from the UE unit.

In one other option, the added device is a receive-only device. Thus, the size/weight and cost of the apparatus is very low.

The combination of an implementation that obtains accurate DLOS distance measurements and an implementation that can make accurate DLOS AOA determinations would greatly improve the cell ID + RTT tracking positioning method accuracy, i.e., 10 times or more. Another advantage of this approach is that a single tower can be utilized to determine the UE location at any time (without the need to place the UE in soft handover mode). Because the exact location fix can be obtained with a single tower, there is no need to synchronize multiple cell towers. Another option for determining DLOS AOA is to use an existing eNB antenna array and eNB equipment. This option may further reduce the cost of implementing the improved cell ID + RTT method. However, because the eNB antenna is not designed for positioning applications, positioning accuracy may be reduced. In addition, network operators may be reluctant to implement the required changes in the base station (software/hardware).

In LTE (evolved universal terrestrial radio access (E-UTRA), physical channels and modulation, 3GPP TS 36.211 release 9 technical specification), Positioning Reference Signals (PRS) are added. These signals will be used by the UE for downlink OTDOA (DL-OTDA) positioning. In addition, this release 9 requires eNB synchronization. Thus, the last obstacle of the OTDOA method is cleared (see paragraph 274 above). PRS improves UE listening capability of multiple enbs at the UE. Note that: release 9 does not specify eNB synchronization accuracy (some proposals: 100 ns).

U-TDOA/UL-TDOA is in the research stage; will be standardized in 2011.

The DL-OTDOA Method (version 9) is described in detail in US patent 2011/0124347A1(Method and Apparatus for UE position in LTE Network, Chen et al). The 9 th release DL-OTDOA is subject to multipath. Some of the multipath mitigation may be achieved via increased PRS signal bandwidth. However, the trade-off is to increase the longer time between scheduling complexity and UE location fix. Furthermore, for networks with limited operating bandwidth (e.g. 10MHz), the best possible accuracy is 100 meters, see Chen, table 1.

The above numbers are the most likely cases. Other situations, especially when the DLOS signal strength is significantly lower (10dB to 20dB) compared to the reflected signal strength, result in the above positioning/ranging error being significantly larger (2 to 4 times).

The embodiments described herein allow for ranging/positioning accuracy improvements of up to 50 times for a given signal bandwidth over the performance achieved by the Chen et al DL-OTDOA and UL-PRS methods described in the background section. Thus, applying embodiments of the methods described herein to version 9 PRS processing will reduce positioning errors to as low as 3 meters or less in 95% of all possible cases. Furthermore, this accuracy gain will reduce scheduling complexity and reduce the time between UE location fixes.

Further improvements of the OTDOA method are possible using the embodiments described herein. For example, ranging to the serving cell may be determined from signals of other serving cells, thereby improving the listening of neighboring cells and reducing scheduling complexity, including the time between UE location fixes.

Embodiments also provide up to a 50 fold improvement in the accuracy of the U-TDOA method and UL-TDOA from Chen et al, described in the background section. Applying the embodiment to Chen's UL-TDOA variant reduces the location error to as low as 3 meters or less in 95% of all possible cases. Furthermore, this accuracy gain further reduces scheduling complexity and reduces the time between UE location fixes.

Also, with this embodiment, Chen's UL-TDOA method can be improved by up to 50 times in accuracy. Thus, applying this embodiment to the U-TDOA variant of Chen will reduce the location error to as low as 3 meters or less in 95% of all possible cases. Furthermore, this accuracy gain will further reduce the scheduling complexity and reduce the time between UE location fixes.

The DL-TDOA and U-TDOA/UL-TDOA methods described above rely on one-way measurements (ranging). This embodiment and almost all other ranging techniques require that the PRS and/or other signals used in the one-way ranging process will be frequency and phase coherent. OFDM based systems such as LTE are frequency coherent. However, the UE unit and eNB are not in phase or time synchronized to a few nanoseconds by a common source such as UTC, for example, a random phase adder is present.

To avoid the effect of phase coherence on ranging accuracy, embodiments of the multipath processor calculate a differential phase between the ranging signal (e.g., reference signal) and each component (subcarrier). This eliminates the random phase term adder.

As described above in the discussion of Chen et al, application of the embodiments described herein results in a significant improvement in accuracy in an indoor environment as compared to the performance achieved by Chen et al. For example, according to Chen et al, DL-OTDOA and/or U-TDOA/UL-TDOA are primarily used in outdoor environments, indoors (buildings, parks, etc.), and DL-OTDOA and U-TDOA techniques may perform poorly. Note several reasons (see Chen, pages 161 to 164) including the Distributed Antenna System (DAS) commonly used indoors, whereby each antenna does not have a unique ID.

The embodiments described below operate with wireless networks that employ OFDM modulation and/or its derivatives, as well as reference/pilot/and synchronization signals. Thus, the embodiments described below operate with LTE networks, and are also applicable to other wireless systems and other wireless networks, including other types of modulation, with or without reference/pilot/and/or synchronization signals.

The methods described herein are also applicable to other wireless networks, including WiMax, WiFi, and white space. Other wireless networks that do not use reference/pilot signals and/or synchronization signals may employ one or more of the following types of alternative modulation embodiments as described in U.S. patent No. 7872583: 1) wherein a portion of the frame is dedicated to ranging signals/ranging signal elements; 2) wherein the ranging signal elements are embedded in the transmission/reception signal frames; and 3) wherein the ranging signal element is embedded with data.

Embodiments of the multipath mitigation range estimation algorithm described herein (also described in U.S. patent nos. 7969311 and 8305215) operate by providing an estimate of the concentrated range consisting of the direct path of the signal (DLOS) plus multipath reflections.

The LTE DAS system generates multiple copies of the same signal to mobile receivers (UEs) seen at various time offsets. The delay is used to uniquely determine the geometric relationship between the antenna and the mobile receiver. The signal seen by the receiver is similar to that seen in a multipath environment, except that the main "multipath" component results from the sum of the offset signals from multiple DAS antennas.

The receiver sees the same signal set as the type of signal set implementation designed to take advantage of, except that in this case the dominant multipath component is not a conventional multipath component. The present multipath mitigation processor (algorithm) is able to determine the DLOS and the attenuation and propagation delay of each path (e.g., reflection) (see equations 1-3 and associated description). While multipath may exist due to dispersive RF channels (environment), the dominant multipath component in this signal set is associated with transmissions from multiple antennas. Embodiments of the present multipath algorithm may estimate these multipath components, isolate the range of the DAS antennas from the receivers, and provide the range data to a position processor (implemented in software). Depending on the antenna placement geometry, the solution may provide X, Y position coordinates and X, Y, Z position coordinates.

Thus, the present embodiment does not require any hardware and/or new network signal additions. Furthermore, the positioning accuracy can be significantly improved by: 1) suppression of multipath, and 2) in the case of an effective DAS, the lower limit of positioning error can be significantly reduced, such as from about 50 meters to about 3 meters.

It is assumed that the position (location) of each antenna of the DAS is known. The signal propagation delay for each antenna (or relative to the other antennas) must also be determined (known).

For an active DAS system, the signal propagation delay can be automatically determined using a loopback technique whereby a known signal is sent back and forth and the round trip time is measured. The loopback technique also eliminates variations (drift) in signal propagation delay with temperature, time, etc.

The use of multiple macrocells and associated antennas, picocells and microcells further improves resolution by providing additional reference points.

The implementation of the various range estimates in the signal set from multiple copies of multiple antennas described above may be further enhanced by changing the signal transmission structure in the following two ways. The first way is to time-multiplex the transmissions from each antenna. The second method is to frequency multiplex each of the antennas. And the distance measurement and position accuracy of the system are further improved by using enhancement, time and frequency multiplexing. Another approach is to add a propagation delay to each antenna. The delay value will be selected to be large enough to exceed the delay spread in a particular DAS environment (channel), but smaller than the Cyclic Prefix (CP) length, so that multipath caused by the additional delay will not cause inter-symbol interference (ISI).

The addition of a unique ID or unique identifier for each antenna increases the efficiency of the resulting solution. This eliminates the need for the processor to estimate all ranges from the signal of each of the antennas, for example.

In one embodiment utilizing the LTE downlink, one or more reference signal subcarriers, including pilot and/or synchronization signal subcarriers, are used to determine subcarrier phases and amplitudes, which are then applied to a multipath processor for multipath interference suppression and generation of range-based position observable, and a multipoint positioning and position consistency algorithm is used to position the estimate to compile the wild point.

Another embodiment makes use of the fact that: the LTE uplink signaling also includes reference signals (mobile to base), and also contains reference subcarriers. In fact, there is more than one mode, including these subcarriers from the full sounding mode used by the network to allocate a frequency band to the uplink device, where the reference subcarriers are used to generate a channel impulse response to help demodulate the uplink signal, etc. In addition, similar to the DL PRS added in release 9, additional UL reference signals may be added in the upcoming and future standard versions. In this embodiment, the uplink signals are processed by multiple base stations (enbs) using the same range-phase, multipath mitigation process to generate range-correlated observable objects. In this embodiment, a location consistency algorithm established by a multi-point localization algorithm is used to compile the outlier observable and generate a location estimate.

In another embodiment, one or more reference (including pilot and/or synchronization) subcarriers for both LTE downlink and LTE uplink are collected, range-phase mapping is applied, multipath mitigation is applied, and observable objects associated with the range are estimated. These data will then be fused so that a more stable set of observable objects will be provided for the location using a multi-point localization algorithm and a location consistency algorithm. Since the two different frequency bands, downlink and uplink, or Time Division Duplex (TDD) case, improves system coherence, the advantage would be redundancy leading to improved accuracy.

In a Distributed Antenna System (DAS) environment, where multiple antennas transmit the same downlink signal from a microcell, a position consistency algorithm is extended to isolate the range of the DAS antennas from observable objects generated by multipath mitigation processing of reference (including pilot and/or synchronization) signal subcarriers, and to obtain position estimates from the range of multiple DAS transmitters (antennas).

In a DAS system (environment), accurate position estimates are only possible if the signal paths from the individual antennas can be resolved with high accuracy (10 meters or more of accuracy), so that the path errors are only a fraction of the distance between the antennas. Because none of the prior art techniques/methods can provide such accuracy in a heavy multipath environment (signals from multiple DAS antennas would appear to induce heavy multipath), the prior art techniques/methods cannot take advantage of the above-described extension of the position consistency algorithm and such positioning methods/techniques in a DAS environment.

The inviisitrack multipath mitigation method and system for object identification and location discovery described in U.S. patent No. 7872583 applies to signal phase mapping ranges, utilizes both LTE downlink, uplink, and/or (downlink and uplink) to generate multipath interference mitigation and procedures for range-based location observable objects, one or more reference signals, and uses multilateration and location consistency to generate location estimates.

In all of the above embodiments, a three-point location algorithm may also be employed.

DL-OTDOA positioning is specified in LTE release 9: evolved universal terrestrial radio access (E-UTRA); physical channels and modulation; 3GPP TS 36.211 release 9 technical specification. However, DL-OTDOA positioning has not been implemented by wireless carriers (carriers). Meanwhile, downlink positioning may be achieved within the current (e.g., unmodified) LTE network environment by using existing physical layer measurement operations.

In LTE, the UE and eNB need physical layer measurements of radio characteristics. The measurement definitions are specified in 3GPP TS 36.214. These measurements are performed periodically and reported to higher layers and are used for various purposes including intra-and inter-frequency handovers, inter-radio access technology (inter-RAT) handovers, timing measurements and other purposes to support Radio Resource Management (RRM).

For example, Reference Signal Received Power (RSRP) is the average of the power of all resource elements carrying cell-specific reference signals over the entire bandwidth.

Another example is a Reference Signal Received Quality (RSRQ) measurement (RSRQ combined signal strength and interference level) that provides additional information.

The LTE network provides the UE with an eNB neighbor (to serving eNB) list. Based on the network knowledge configuration, the (serving) eNodeB provides the UE with an identifier of the neighboring eNB, etc. The UE then measures the signal quality of its receivable neighbors. The UE reports the results back to the eNodeB. Note that: the UE also measures the signal quality of the serving eNB.

According to the present description, RSRP is defined as the linear average of the power contributions (in [ W ]) of the resource elements carrying cell-specific reference signals within the considered measurement frequency bandwidth. The measurement bandwidth used by the UE to determine RSRP is decided by the UE implementation, with the constraint that the corresponding measurement accuracy requirement has to be met.

The bandwidth is quite large in view of measurement bandwidth accuracy requirements, and the cell-specific reference signals used in RSRP measurement can be further processed to determine these reference signal subcarrier phases and amplitudes, which are then applied to a multipath processor for multipath interference suppression and range-based position observable generation. In addition, other reference signals used in RSRP measurement, such as Secondary Synchronization Signals (SSS), may also be used.

Thereafter, based on the range observable objects from the three or more cells, a position fix and position consistency algorithm may be used to estimate a position fix.

As mentioned before, although there are several reasons that cause the RF fingerprint database to be unstable, one of the main reasons is multipath (RF signatures are very sensitive to multipath). Thus, RF fingerprinting methods/techniques location accuracy are heavily influenced by multi-path dynamics — changes over time, environment (e.g., weather), human and/or object movement (including fluctuating uncertainties): variability > 100% depends on device Z-height and/or antenna orientation (see Tsung-Han Lin et al, "microscopic examination of RSSI-signature based indoor positioning systems").

This embodiment may significantly improve RF fingerprinting accuracy due to the ability to find and characterize each individual path (including significantly attenuated DLOS). Thus, RF fingerprint decisions regarding location fixes may be supplemented with real-time multipath profile information.

As mentioned above, locating the position will require position reference time synchronization. In wireless networks, these location references may include access points, macro/mini/pico and femto cells, and so-called small cells (enbs). However, wireless carriers do not implement the synchronization accuracy required for accurate location fixes. For example, in the case of LTE, the standard does not require any time synchronization between enbs for Frequency Division Duplex (FDD) networks. For time division duplex (LTE TDD), the time synchronization accuracy is limited to +/-1.5 microseconds. This amounts to a positioning uncertainty of more than 400 meters. Although not required, LTE FDD networks are also synchronous, but use an even greater (greater than 1.5 microseconds) limit.

Wireless LTE operators are using GPS/GNSS signals to synchronize enbs in frequency and time. Note that: LTE enbs must maintain very accurate carrier frequencies for macro/small cells: 0.05ppm and less accurate for other types of cells, 0.1ppm to 0.25 ppm. The GPS/GNSS signals can also achieve the required (for positioning) time synchronization accuracy of better than 10 nanoseconds. However, network operators and network equipment manufacturers attempt to reduce the costs associated with GPS/GNSS units by employing Network Time Protocol (NTP) and/or Precision Time Protocol (PTP), such as IEEE 1588v2 PTP, to facilitate packet transport/internet/ethernet networking time synchronization, for example.

IP network based synchronization is likely to meet minimum frequency and time requirements but lacks the GPS/GNSS accuracy required for position location.

The methods described herein are based on GPS/GNSS signals and signals generated by an eNB and/or AP or other wireless network device. The method may also be based on IP networking synchronization signals and protocols and signals generated by the eNB and/or AP or other wireless network device. The method is also applicable to other wireless networks including WiMax, WiFi and white space.

The eNB signal is received by a time observation unit (TMO) installed at the operator's eNB facility (fig. 12). The TMO also includes an external synchronization source input.

The eNB signal is processed by the TMO and time stamped using a clock synchronized to the external synchronization source input.

The external synchronization source may be from a GPS/GNSS and/or internet/ethernet network, e.g. PTP or NTP, etc.

Time stamped signals, e.g. LTE frame start signals (which may be other signals, especially in other networks), also including eNB (cell) location and/or cell ID, are sent via the internet/ethernet backhaul to a central TMO server, which creates, maintains and updates a database of all enbs.

The UEs and/or enbs involved in the ranging and obtaining the location fix will query the TMO server and the server will return the time synchronization offset between the involved enbs. These time synchronization offsets will be used by the UE and/or the eNB involved in the process of acquiring the location fix to adjust the location fix.

Alternatively, the position fix calculation and adjustment may be performed by the TMO server when the UE and/or eNB involved in the ranging procedure will also provide the obtained ranging information to the TMO server. The TMO server will then return an accurate (adjusted) position (location) fix.

If more than one cell eNB device is co-located together, a single TMO may process and time stamp signals from all enbs.

Round Trip Time (RTT) measurements (ranging) may be used for positioning. The drawback is that RTT ranging is subject to multipath which has a significant impact on positioning accuracy.

RTT positioning, on the other hand, does not typically require position reference synchronization (in time), especially in the case of LTE, the eNB does not.

Meanwhile, when operating with pilot/reference signals and/or other signals of the wireless network, the multipath mitigation processors, methods/techniques and algorithms described in U.S. patent No. 7872583 are capable of determining the channel response of the RTT signal, e.g., identifying the multipath channel through which the RTT signal travels. This allows correction of the RTT measurement so that the actual DLOS time will be determined.

With DLOS time known, it is possible to obtain a position fix using three-point positioning and/or similar positioning methods without requiring eNB or position reference time synchronization.

Even if the TMO and TMO server are in place, the technological integration of invimitrack would require changes to macro/mini/pico and small cells and/or UEs (mobile phones). While these changes are limited to software/firmware (SW/FW), a significant amount of effort is expended to restore the existing infrastructure. Additionally, in some cases, the network operator and/or UE/mobile phone manufacturer/vendor are resistant to device modification. Note that: the UE is a wireless network user equipment.

Such SW/FW changes can be avoided entirely if the TMO and TMO server functionality is extended to support the inviitrack positioning technique. In other words, another embodiment described below operates with wireless network signals, but does not require any modification to the wireless network equipment/infrastructure. Thus, the embodiments described below operate with LTE networks, and are also applicable to other wireless systems/networks, including Wi-Fi.

In essence, this embodiment creates a parallel wireless location infrastructure that uses wireless network signals to obtain a location fix.

Similar to TMO and TMO servers, the InvisiTrack's location infrastructure will consist of one or more wireless Network Signal Acquisition Units (NSAUs) and one or more Location Server Units (LSUs) that collect and analyze data from the NSAUs, determine ranges and locations, and convert them into a table of, for example, phone/UE IDs and instantaneous locations. The LSU is connected to the wireless network through an API of the network.

Multiple ones of these units may be deployed in various locations in a large infrastructure. If the NSAU has coherent timing, the results of all NSAUs can be used, which will provide better accuracy.

The coherent timing may be derived from a GPS clock and/or other stable clock source.

The NSAUs communicates with the LSU via a Local Area Network (LAN), a Metro Area Network (MAN), and/or the internet.

In some installations/examples, NSAUs and LSUs may be combined/integrated into a single unit.

To support location services using LTE or other wireless networks, the transmitters need to synchronize clocks and events to within tight tolerances. Typically, this is achieved by locking to the 1PPS signal of the GPS. This will result in the timing in the local region being synchronized to within 3 nanoseconds (1-sigma).

However, there are many instances where this type of synchronization is impractical. The present embodiment provides for time offset estimation and tracking of time offsets between downlink transmitters in order to provide delay compensation values to the positioning process, so the positioning process can continue as if the transmitters were clock and event synchronized. This is achieved by a priori knowledge of the transmitting antenna (which is required for any location service) and the receiver with known a priori antenna locations. This receiver, called a synchronization unit, will collect data from all downlink transmitters and, in view of its knowledge of the position, calculate the offset timing from the pre-selected base antenna. These offsets are tracked by the system by using a tracking algorithm that compensates for clock drift of the downlink transmitter. Note that: the process of deriving the pseudoranges from the received data will utilize the invitrack multipath mitigation algorithm (described in U.S. patent No. 7872583). Therefore, synchronization will not be affected by multipath.

The position processor (positioning server, LSU) uses these offset data to properly align the data from each downlink transmitter so that it appears that the data has been generated by the synchronous transmitter. Time accuracy is comparable to optimal 1-PPS tracking and will support 3 meter position accuracy (1-sigma).

The synchronous receiver and/or the antenna of the receiver will be positioned based on the best GDOP for best performance. In large installations, multiple synchronous receivers may be utilized to provide an equivalent 3 nanosecond (1-sigma) synchronization shift throughout the network. By utilizing a synchronization receiver, the requirement for downlink transmitter synchronization is eliminated.

The synchronization receiver unit may be a separate unit communicating with NSAU and/or LSU. Alternatively, the synchronization receiver may be integrated with the NSAU.

An exemplary wireless network location device is illustrated in fig. 13.

An implementation of a fully autonomous system (no client network investment) with LTE signals operates in the following mode:

1. uplink mode-use of wireless network Uplink (UL) signals for positioning (fig. 16 and 17).

2. Downlink mode-use of wireless network Downlink (DL) signals for positioning (fig. 14 and 15).

3. Bidirectional mode-positioning using both UL and DL signals.

In uplink mode, multiple antennas are connected to one or more NSAUs. These antenna locations are independent of the wireless network antenna; the NSAU antenna positions are selected to minimize the geometric dilution of precision (GDOP).

Network RF signals from the UE/mobile phone device are collected by the NSAU antenna and processed by the NSAU to produce time stamped processed network RF signal samples during a time interval sufficient to capture one or more instances of all signals of interest.

Optionally, the NSAU will also receive, process and time stamp samples of the downlink signal to obtain additional information, e.g., for determining UE/phone ID, etc.

From the captured timestamp samples, the UE/mobile phone device identification number (ID) and the timestamp wireless network signal of interest associated with each UE/cell phone ID will be determined (obtained). This operation may be performed by NSAU or by LSU.

NSAU will periodically provide data to the LSU. If one or more UE/mobile phone IDs require unscheduled data, the LSU will request additional data.

No changes/modifications are required in the wireless network infrastructure and/or existing UEs/mobile phones for UL mode operation.

In Downlink (DL) mode, an inviistrack enabled UE will be required. In addition, if a phone is used to obtain a location fix, the mobile phone FW must be modified.

In some cases, an operator may make baseband signals available from a base station unit (BBU). In this case, the NSAU will also be able to process these available baseband wireless network signals, rather than RF wireless network signals.

In DL mode, there is no need to associate the UE/cell phone ID with one or more wireless network signals, as these signals will be processed in the UE/mobile phone or the UE/cell phone will periodically generate time-stamped samples of the processed network RF signals and send these signals to the LSU; and the LSU sends the result back to the UE/handset.

In DL mode, NSAU will process RF or baseband (when available) wireless network signals and time stamp the processed RF or baseband wireless network signals. From the captured time stamp samples, the wireless network signal DL frame start associated with the network antenna will be determined (obtained) and the difference (offset) between these frame starts will be calculated. This operation may be performed by NSAU or by LSU. The frame start offset for the network antenna will be stored on the LSU.

In DL mode, the frame start offset of the network antenna is sent from the LSU to the UE/telephony device in case the device is to process/determine its own position fix using the invitrack technique. Otherwise, when the UE/cellular telephone device will periodically send time-stamped processed network RF signal samples to the LSU, the LSU will determine the location fix of the device and send this location fix data back to the device.

In the DL mode, wireless network RF signals will come from one or more wireless network antennas. To avoid multipath effects on the accuracy of the results, RF signals should be sniffed from the antenna or the connection of the antenna to the wireless network device.

The bidirectional mode encompasses determining a position fix from both UL and DL operations. This allows further improvement of the positioning accuracy.

Some enterprise settings use one or more BBUs that feed one or more Remote Radio Heads (RRHs), where each RRH in turn feeds multiple antennas with the same ID. In such an environment, it may not be necessary to determine the DL mode frame start offset for the network antenna, depending on the wireless network configuration. This includes a single BBU setting and multiple BBUs, whereby the antennas of each BBU are assigned to a certain zone and the coverage of adjacent zones overlap.

On the other hand, a configuration in which antennas fed from multiple BBUs are staggered within the same area would require a determination of the network antenna start offset of the DL mode frame.

In a DL mode operating in a DAS environment, multiple antennas may share the same ID.

In an embodiment, a position consistency algorithm is extended/developed to isolate the range of DAS antennas from observable objects generated by multipath mitigation processing of reference (including pilot and/or synchronization) signal subcarriers, and to obtain position estimates from multiple DAS transmitter (antenna) ranges.

However, these consistency algorithms have a limit on the number of antennas transmitting the same ID. The number of antennas transmitting the same ID can be reduced by:

1. interleaving antennas fed from different sectors of a sectorized BBU for a given coverage area (BBU capable of supporting up to six sectors)

2. Interleaving antennas fed from different sectors of a sectorized BBU and antennas fed from different BBUs for a given coverage area

3. A propagation delay element is added to each antenna. The delay value will be selected to be large enough to exceed the delay spread in a particular DAS environment (channel), but smaller than the Cyclic Prefix (CP) length, so that multipath caused by the additional delay will not cause inter-symbol interference (ISI). Adding a unique delay ID to one or more antennas further reduces the number of antennas transmitting the same ID.

In one embodiment, an autonomous system may be provided that does not have a client network investment. In such embodiments, the system may operate on frequency bands other than the LTE frequency band. For example, the U.S. industrial scientific and medical applications (ISM) band and/or the white space band may be used where LTE services are not available.

This embodiment may also be integrated with macro/mini/pico/femto sites and/or UE (mobile phone) devices. While integration may require customer network investment, the integration may reduce cost overhead and may significantly improve Total Cost of Ownership (TCO).

As mentioned above, the UE may use PRS for downlink observed time difference of arrival (DL-OTDOA) positioning. With respect to synchronization of neighboring base stations (enbs), 3GPP TS 36.305 (phase 2 functional specification for User Equipment (UE) positioning in E-UTRAN) specifies the timing of transmissions to a UE, which is served relative to the eNodeB of a candidate cell (e.g., a neighboring cell). 3GPP TS 36.305 also specifies the Physical Cell ID (PCI) and Global Cell ID (GCIs) of the candidate cell for measurement purposes.

This information is delivered from an enhanced serving mobile location center (E-MLC) server according to 3GPP TS 36.305. It should be noted that TS 36.305 does not specify the timing accuracy described above.

In addition, 3GPP TS 36.305 specifies that the UE will return downlink measurements to the E-MLC, including Reference Signal Time Difference (RSTD) measurements.

RSTD is a measurement made at a pair of eNBs (see TS 36.214 evolved Universal terrestrial radio Access (E-UTRA); physical layer measurements; release 9). The measurement is defined as the relative timing difference between a subframe received from the neighbor cell j and the corresponding subframe of the serving cell i. The positioning reference signals are used to make these measurements. The results are reported back to the location server where the position was calculated.

In one embodiment, a hybrid approach may be defined to accommodate both newly introduced PRSs and already existing reference signals. In other words, the hybrid method may be used/operated with PRS, with other reference signals (e.g., cell or node specific reference signals (CRS)), or with both signal types.

This hybrid approach provides the advantage of allowing the network operator to dynamically select an operating mode based on environmental or network parameters. For example, PRS have better listening than CRS, but can result in a reduction in data throughput of up to 7%. On the other hand, the CRS signal does not cause any throughput reduction. Furthermore, the CRS signal is backward compatible with all previous LTE releases (e.g.,release 8 and lower). Thus, the hybrid approach provides the network operator with the ability to trade off or balance between snooping, throughput, and compatibility.

In Long Term Evolution (LTE) implementations, LTE downlink baseband signals (generated by cells or wireless nodes and referred to herein as "nodes") are typically combined into downlink frames. A receiver for detecting and receiving such signals may detect downlink frames from multiple cells or nodes (two or more). Each downlink frame includes a plurality of CRSs or reference signals. In a Downlink (DL) frame, these reference signals have predetermined positions in time and frequency, e.g., there is a certain time offset between the start of the frame and each CRS in a given frame.

Furthermore, each CRS is modulated with a special code. The modulation and code are also predetermined. The CRS modulation is the same for all nodes, but the code (seed) is determined by the number of IDs (identifications) of the nodes.

Therefore, by knowing the node ID, the directional position of the frame start time of each frame from each node (cell) in the spectrum of the reference signal can be estimated. For this purpose, the start of frame time or start of frame of all DL signals from different nodes needs to be determined first. For example, in one embodiment, by correlating the received DL baseband signal with known copies of code modulated CRS (internally generated by the detector and/or multipath mitigation processor), all CRS sequences or other reference signals from various nodes may be found, and this information used to find the coarse position frame start for all observable nodes. In one embodiment, the detector may also demodulate/decode the CRS and then associate the demodulated/decoded CRS with the baseband subcarriers allocated to the CRS.

Also, in one embodiment, the CRS may also be used as a ranging signal by the multipath mitigation processor. Thus, in addition to finding the coarse frame start, the correlation process of the detector can also isolate the CRS from other signals in the frame (such as the payload) using the code used to modulate these signals. Thereafter, the isolated CRSs and associated frame starts are transmitted to a multipath mitigation processor for ranging.

A similar approach may be used in uplink mode whereby timing offsets between different node receivers may be determined.

In a downlink embodiment, a system for tracking and locating one or more wireless network devices in communication with a network includes a user equipment receiver configured to receive a plurality of signals from two or more nodes in communication with the network, the plurality of signals modulated with a code determined by an identity of each of the two or more nodes transmitting the plurality of signals, the user equipment receiver including a detector configured to detect and isolate a reference signal from the plurality of signals based on the identification, and a processor configured to use the reference signal as a ranging signal from each node for tracking and locating the one or more wireless network devices.

In this embodiment, wherein the plurality of signals from each of the two or more nodes are combined into a frame comprising the reference signal, and wherein the detector is further configured to estimate a directional position of a start of the frame from each node.

In this embodiment, wherein the detector is further configured to estimate the directional position by associating the reference signal with a known copy of such reference signal.

In this embodiment, wherein the detector is further configured to isolate the reference signal from any other signal in the frame, and wherein the detector is further configured to isolate the reference signal of each of the two or more nodes.

In this embodiment, the processor is at least one multipath mitigation processor, and wherein the multipath mitigation processor is configured to receive the directional position and the isolated reference signal and to estimate the relative time of arrival of the ranging signal from each node.

In this embodiment, wherein the processor is at least one multipath mitigation processor.

In this embodiment, wherein the plurality of signals from each of the two or more nodes are in a frame, wherein the detector is further configured to estimate a directional position from the start of the frame for each node, wherein the detector is configured to isolate the reference signal from any other signal in the frame, wherein the detector is further configured to isolate the reference signal for each of the two or more nodes, wherein the detector is configured to pass the directional position and the isolated reference signal for each node to the multipath mitigation processor, and wherein the multipath mitigation processor is configured to receive the heading position and the isolated reference signal and to estimate a relative time of arrival of the ranging signal from each node.

In this embodiment, the system further includes an uplink embodiment, wherein the node receiver is configured to receive a device signal from one or more wireless network devices, the device signal being modulated with a device code determined by a device identification of each of the one or more wireless network devices transmitting the device signal, the node receiver includes a device detector configured to detect and isolate a device reference signal from the device signal based on the device identification, and the second processor is configured to use the device reference signal as a ranging signal from each wireless network device for tracking and locating the one or more wireless network devices.

In one embodiment, a system for tracking and locating one or more wireless network devices in communication with a network includes a user equipment receiver configured to receive a plurality of signals from two or more nodes in communication with the network, the plurality of signals modulated with a code determined by an identity of each of the two or more nodes transmitting the plurality of signals, and a processor configured to detect and isolate a reference signal from the plurality of signals based on the identification and configured to use the reference signal as a ranging signal from each node for tracking and locating the one or more wireless network devices.

In this embodiment, wherein the plurality of signals from each of the two or more nodes are combined into a frame comprising the reference signal, and wherein the processor is further configured to estimate a directional position of a start of the frame from each node.

In this embodiment, wherein the processor is further configured to estimate the directional position by associating the reference signal with a known copy of the reference signal.

In this embodiment, wherein the processor is further configured to estimate the relative time of arrival of the ranging signal from each node based on the directional position and the isolated reference signal.

In this embodiment, wherein the processor is further configured to isolate the reference signal from any other signal in the frame, and wherein the processor is further configured to isolate the reference signal of each of the two or more nodes.

In this embodiment, wherein the plurality of signals from each of the two or more nodes are in a frame, wherein the processor is further configured to estimate a directional position of a start of frame from each node by associating the reference signal with a known copy of the reference signal, wherein the processor is further configured to isolate the reference signal from any other signals in the frame and to isolate the reference signal for each of the two or more nodes, and wherein the processor is further configured to estimate the relative time of arrival of the ranging signal from each node based on the directional position and the isolated reference signal.

In one embodiment, a system for tracking and locating one or more wireless network devices in communication with a network includes a detector configured to receive a plurality of signals from two or more nodes in communication with the network, the plurality of signals modulated with a code determined by an identification of each of the two or more nodes transmitting the plurality of signals, and the detector configured to detect and isolate a reference signal from the plurality of signals based on the identification, and the processor configured to use the reference signal as a ranging signal from each node for tracking and locating the one or more wireless network devices.

In this embodiment, wherein the plurality of signals from each of the two or more nodes are combined into a frame comprising the reference signal, and wherein the detector is further configured to estimate a directional position of a start of the frame from each node.

In this embodiment, wherein the detector is further configured to estimate the directional position by associating the reference signal with a known copy of such reference signal.

In this embodiment, wherein the detector is further configured to isolate the reference signal from any other signal in the frame, and wherein the detector is further configured to isolate the reference signal of each of the two or more nodes.

In this embodiment, the processor is at least one multipath mitigation processor, and wherein the multipath mitigation processor is configured to receive the directional position and the isolated reference signal and to estimate the relative time of arrival of the ranging signal from each node.

In this embodiment, wherein the processor is at least one multipath mitigation processor.

In this embodiment, wherein the plurality of signals from each of the two or more nodes are in a frame, wherein the detector is further configured to estimate a directional position from the start of the frame for each node, wherein the detector is configured to isolate the reference signal from any other signal in the frame, wherein the detector is further configured to isolate the reference signal for each of the two or more nodes, wherein the detector is configured to pass the directional position and the isolated reference signal for each node to the multipath mitigation processor, and wherein the multipath mitigation processor is configured to receive the heading position and the isolated reference signal and to estimate a relative time of arrival of the ranging signal from each node.

In one embodiment, a system for tracking and locating one or more wireless devices in communication with a network includes a node receiver configured to receive a device signal from one or more wireless network devices, the device signal modulated with a device code determined by a device identification of each of the one or more wireless network devices transmitting the device signal, the node receiver including a device detector configured to detect and isolate a device reference signal from the device signal based on the device identification, and the processor configured to use the device reference signal as a ranging signal from each wireless network device for tracking and locating the one or more wireless network devices.

Furthermore, the hybrid approach may be apparent for LTE UE positioning architectures. For example, the hybrid approach may operate in the 3GPP TS 36.305 framework.

In one embodiment, the RSTD may be measured and transmitted from the UE to the E-SMLC in accordance with 3GPP TS 36.305.

UL-TDOA (U-TDOA) is currently in the research phase and is expected to be standardized in theupcoming release 11.

The UL-TDOA (uplink) embodiment is described above and is also shown in fig. 16 and 17. Fig. 18 and 19, described below, provide examples of alternative embodiments of UL-TDOA.

Fig. 18 presents an environment that can include one or more DAS and/or femto/small cell antennas. In this exemplary embodiment, each NSAU is equipped with a single antenna. As shown, at least three NSAUs are required. However, additional NSAUs may be added to improve the listening, as each UE must be "listened" by at least three NSAUs.

Further, NSAUs may be configured as receivers. For example, each NSAU receives but does not transmit information over the air. In operation, each NSAU may listen for wireless uplink network signals from the UE. Each of the UEs may be a cellular phone, a tag device, and/or another UE apparatus.

Further, the NSAU may be configured to communicate with a Location Server Unit (LSU) over an interface, such as a wired service or LAN. In turn, the LSU may communicate with a wireless network or an LTE network. The communication may be via a network API, where the LSU may communicate with E-SMLC of the LTE network, for example, and may use wired services such as LAN and/or WAN.

Optionally, the LSU may also communicate directly with the DAS base station and/or femto/small cells. The communication may use the same or a modified network API.

In this embodiment, Sounding Reference Signals (SRS) may be used for positioning purposes. However, other signals may be used.

The NSAU may convert the UE uplink transmission signal into a digital format, e.g., I/Q samples, and may periodically transmit a plurality of converted signals to the LSU with a time stamp.

The DAS base station and/or femto/small cell may communicate one or all of the following data to the LSU:

1) SRS, I/Q samples and timestamps;

2) a list of serving UE IDs; and

3) SRS scheduling for each UE with a UE ID, the scheduling including SRS scheduling request configuration information and SRS-UL-configuration information.

The information passed to the LSU may not be limited by the above information. This information may include any information needed to associate each UE device uplink signal (such as UE SRS) with each UE ID.

The LSU functions may include ranging calculations and obtaining a location fix for the UE. These determinations/calculations may be based on information communicated to the LSU from NSAUs, DAS base stations, and/or femto/small cells.

The LSU may also determine a timing offset of available downlink transmission information passed from the NSAU to the LSU.

The LSU may then provide UE location fixes and other calculations and data for the wireless network or LTE network. Such information may be communicated via a network API.

For synchronization purposes, each NSAU may receive, process, and time stamp samples of the downlink signal. Each NSAU may also periodically send a plurality of such samples, including a timestamp, to the LSU.

Additionally, each NSAU may include an input configured to synchronize with an external signal.

FIG. 19 depicts another embodiment of UL-TDOA. In addition to the components shown in fig. 18, the environment of this embodiment may include one or more cell towers that may be used in place of DAS base stations and/or femto/small cells. Data from one or more cell towers may be used to obtain a location fix for the UE.

Thus, an advantage of this embodiment includes using only a single cell tower (eNB) to obtain the location fix. Further, this embodiment may be configured to operate in a similar manner as described in fig. 18, except that one or more enbs may replace the DAS base stations and/or femto/small cells.

One method of uplink positioning of a UE is a cell identification method (CID). In the basic CID method, the UE position may be determined on a cell level. The method is purely network based. Thus, the UE (e.g., handheld terminal) is unaware of the fact that it is being tracked. While this is a relatively simple method, this method lacks accuracy because the positioning uncertainty is equal to the cell diameter. For example, as shown in fig. 20, anyhandheld terminal 2000 within thecell diameter 2002 of the servingcell tower 2004 effectively has the same location, even though they are not at the same location. When combined with serving sector identification (sector ID) knowledge, the accuracy of the CID method can be improved. For example, as shown in fig. 21, sector ID2100 identifies asector 2102 withincell diameter 2002 that includes a plurality ofhandheld terminals 2104 that are known to have different locations than otherhandheld terminals 2000 in other sectors ofcell diameter 2002.

It is possible to further enhance the CID method by an enhanced cell ID (E-CID) method, which provides a further improvement over the basic CID method described above. One enhancement uses timing measurements to calculate how far away a UE is from an eNB (network node). The distance may be calculated as half Round Trip Time (RTT) or Timing Advance (TA) in LTE (LTE TA) multiplied by the speed of the light. The RTT or TA may be used for range estimation if the UE is connected. In this case: the serving cell tower or sector and the UE (according to the serving eNB commands) will measure the timing difference between the Rx subframe and the Tx subframe. The UE reports the timing difference measurement to the eNB (also under eNB control). It should be noted that LTE release 9 addsTA type 2 measurements that rely on the timing advance estimated from the received PRACH preamble during the random access procedure. A physical/Packet Random Access Channel (PRACH) preamble specifies a maximum number of preambles to transmit during one PRACH ramping period when no response is received from a tracked UE. LTE type 1TA measurement is equivalent to RTT measurement, as follows:

RTT (type 1) is eNB (Rx-Tx) + UE (Rx-Tx)

By knowing the coordinates of the eNB and the height of the serving cell tower antenna, the location of the UE can be calculated by the network.

However, the E-CID positioning method is still limited because in one dimension, the positioning accuracy depends on the sector width and distance from the serving cell tower, and in the other dimension, the error depends on the ta (rtt) measurement accuracy. The sector width varies with the network topology and is affected by propagation phenomena, in particular multipath. Sector accuracy estimates vary from 200 meters to over 500 meters. The LTE TA measurement resolution is 4Ts, which corresponds to a maximum error of 39 meters. However, due to calibration inaccuracies and propagation phenomena (multipath), the actual error in LTE TA measurements is even larger and can reach as much as 200 meters.

As shown in fig. 22, the E-CID method can be further improved by adding a feature called angle of arrival (AoA). The eNB uses a linear array ofequidistant antenna elements 2200 to estimate the direction from which the UE is transmitting. Typically, the reference signal is used for AoA determination. When reference signals are received from the UE at twoadjacent antenna elements 2200, the reference signals may be phase-rotated by an amount that depends on the AoA, carrier frequency, and element spacing, as shown in fig. 23. AoA will require each eNB to be equipped with an antenna array/adaptive antenna. AoA is also exposed to multipath and topology changes. However, a complex antenna array may significantly reduce thewidth 2202 of thesector 2100, which may result in better positioning accuracy. Furthermore, if two or more serving cell towers 2300 (base stations of enbs equipped with directional antenna arrays) can be used for handheld terminal AoA determination, as shown in fig. 23, the accuracy can be significantly improved. In this case, accuracy is still affected by multipath/propagation phenomena.

Deploying network-wide antenna arrays/adaptive antennas over multiple LTE bands requires a comprehensive effort in terms of capital, time, maintenance, etc. Therefore, antenna arrays/adaptive antennas have not been deployed for UE positioning purposes. Other methods, such as those based on signal strength, do not yield significant accuracy improvements. One such signal strength method is fingerprint identification, which requires the creation and continuous updating of large, continuously changing (over time) fingerprint databases, such as numerous capital and recurring expenses, without significant accuracy improvements. Furthermore, fingerprinting is a UE-based technique whereby the UE location cannot be determined without UE assistance at the UE application level.

Other limited solutions to uplink positioning methods involve the use of AoA capabilities without the need for antenna arrays/adaptive antennas. Such implementations may employ time difference of arrival (TDOA) location techniques for AoA determination, which may be based on estimating differences in arrival times of signals from a source at multiple receivers. A particular value of the time difference estimate defines a hyperbola between two receivers in communication with the UE. When the distance between the receiving antennas is small relative to the distance of the located transmitter (handset), then the TDOA is equal to the angle between the baseline of the sensor (receiver antenna) and the incident RF energy from the transmitter. If the angle between the baseline and true north is known, a line of bearing (LOB) and/or AoA may be determined.

While general location methods using TDOA or LOBs (also known as aoas) are known, TDOA location methods have not been used to determine LOBs because the TDOA reference points are too close to each other to make the accuracy of such techniques acceptable. Instead, LOB is typically determined using directional antennas and/or beamforming antennas. However, the super-resolution approach described herein allows LOB determination using TDOA while significantly improving accuracy. Moreover, without the reference signal processing techniques described herein, reference signals from UEs outside of the serving sector may not be "listened to," e.g., detected, e.g., by the non-serving sector and/or antenna. Without the parsing and processing capabilities described herein, it may not be possible to employ TDOA for LOB determination because at least two reference points, e.g., two or more sectors and/or antennas, are required. Similarly, the UE may not be able to detect reference signals transmitted to the UE from other serving sectors than the serving sector (e.g., from non-serving sectors and/or antennas).

For example, in fig. 24, two antenna separation scenarios are shown: wide separation and near (small) separation. In both cases, thehyperbola 2400 and theincident line 2402 intersect at the handheld terminal 2000 location, but with wider spacing of theantennas 2404, this occurs at a steeper angle, which in turn significantly reduces positioning errors. Meanwhile, with theantennas 2404 close to each other, thehyperbola 2400 becomes interchangeable with theline 2402 or LOB/AoA at which RF energy is incident.

The formula shown below can be used to determine the incident RF energy from the transmitter, where the time difference in arrival time of the RF energy between the two antennas (sensors) is given by:

wherein:

Δ t is the time difference in seconds;

x is the distance between two sensors in meters;

Θ is the angle between the baseline of the sensor and the incident RF wave, in degrees; and

c is the speed of the light.

Several location strategies are available using TDOA location implementations, including: (1) when TDOA measurements (multipoint positioning) between two or more serving cells (e.g., wide separation) are available; (2) when TDOA measurements are only from two or more sectors at one or more serving cells, e.g., small antenna separation, such as LOB/AoA; (3) a combination of strategies (2) and (3); and (4) combinations of TA measurements and strategies (1) to (3), e.g., improved E-CID.

As explained further below, with closely located antennas, TDOA location implementations may use a line of bearing when signals from two or more antennas are from the same cell tower. These signals may be detected in the received composite signal. By knowing the tower location and azimuth angle for each sector and/or antenna, the azimuth and/or AoA azimuth line can be calculated and utilized in the positioning process. LOB/AoA accuracy may be affected by multipath, noise (SNR), etc. However, this effect can be mitigated by the advanced signal processing and multipath mitigation processing techniques described above, which can be based on the super-resolution technique. Such advanced signal processing includes, but is not limited to, signal correlation/correlation, filtering, averaging, synchronous averaging, and other methods/techniques.

The servingcell tower 2500 is typically comprised of a plurality of sectors, as shown in fig. 25, which illustrates a three sector (sector a, sector B, and sector C) configuration. The three sector deployment shown may include one ormore antennas 2502 per sector. A single sector such as sector a can control a UE (handheld terminal) because the handheld terminal transmission will be in sector a's lobe (the center of the lobe coincides with the sector azimuth). Meanwhile, the handset transmission will fall outside the main lobes of sector B and sector C, e.g., into the antenna side lobe. Thus, the handheld terminal signal will still be present in the output signal spectrum of sector B and sector C, but will be significantly attenuated relative to the signal from other handheld terminals located in the main lobe of sector B or sector C. However, by using advanced signal processing, as described above and below, sufficient processing gain can be obtained on the ranging signal so that it can be detected from the side lobes of adjacent sectors, such as sector B and sector C side lobes. For network-based positioning purposes, LTE uplink SRS (sounding reference signal) may be used as a ranging signal.

In other words, while the UE uplink reference signal may be in the side lobe of an adjacent sector antenna, the processing gain through the reference signal processing methods described herein may be sufficient to allow for the calculation of TDOA between the two (or more) sector antennas. The accuracy of this embodiment can be significantly improved by the multipath mitigation processing algorithm described above. Thus, the LOB/AOA that intersects the zone calculated by the LTE TA timing can provide the UE position to within an error ellipse of approximately 20 meters by 100 meters.

Further positioning error reduction may be achieved when the UE may be heard by two or more LTE towers, which is very likely for the processing gain and multipath mitigation techniques described above. In this case, the intersection of the TDOA hyperbola with one or more LOB/AoA lines may result in an error ellipse of 30 meters by 20 meters (for a two sector cell tower). If each cell tower supports three or more sectors, the error ellipse can be further reduced to 10 to 15 meters. If three or more enbs (cell towers) hear the UE, an accuracy of 5 to 10 meters can be achieved. In high value areas such as malls, office parks, etc., additional small cells or passive listening devices may be used to create the necessary coverage.

As described above, one ormore antennas 2502 may be included above each sector of thecell tower 2500. In a typical installation, for a given sector, the signals from each antenna are combined at the receiver input of the sector. Thus, for positioning purposes, two or more sector antennas may be considered a single antenna with a composite directivity pattern, azimuth and elevation. It is assumed that antenna complex directivity and its (main lobe) azimuth and elevation angle can also be assigned to the sector itself.

In one embodiment, the received signals (in digital format) from all sectors of each serving cell tower and neighboring serving cell towers are sent to a Location Server Unit (LSU) for position determination. In addition, each serving sector from each serving cell tower is provided to the LSU according to SRS scheduling and TA measurement results for the serving UE. Assuming that each serving cell tower and each neighboring cell tower location coordinates, the number of sectors per tower with each assumed (composite) sector antenna azimuth and elevation, and each sector location at a cell tower are known, the LSU may determine each UE location relative to the serving cell tower and/or the neighboring cell tower. All of the above information may be sent over a wired network (e.g., LAN, WAN, etc.) using one or more standardized or proprietary interfaces. The LSU may also interface with the wireless network infrastructure using standardized interfaces and/or defined interfaces/APIs of network bearers. The location determination may also be split between the network node and the LSU or performed only in the network node.

In one embodiment, the location determination may be performed in the UE or separate between the UE and the LSU or network node. In such cases, the UE may communicate over the air using standard networking protocols/interfaces. Further, location determination may be performed by a combination of the UE, the LSU, and/or a network node, or the LSU functionality may be implemented (embedded) in a SUPL server, E-SMLC server, and/or LCS (location services) system, which may then be used in place of the LSU.

The Downlink (DL) positioning method embodiment is used interchangeably with the Uplink (UL) positioning embodiment described above. In a DL implementation, a sector can become a transmitter with a transmission mode, azimuth and elevation that matches the sector's reception directivity, azimuth and elevation. Unlike the uplink implementation, in the DL implementation, the UE typically has a single receive antenna. Thus, there is no sensor baseline available for determining RF wave incidence for the UE. However, the UE may determine TDOAs between different sectors, and thus determine hyperbolas between sectors (multi-point positioning), and since the same cell tower sectors are close to each other, the hyperbolas become interchangeable with lines of RF energy incidence or LOB/aoas, as described above with reference to fig. 24, although LOB/AoA accuracy may be affected by multipath, noise (SNR), etc., the effect may be mitigated by using advanced signal processing and multipath suppression processing based on super-resolution techniques as described above.

As noted, UE DL positioning may be achieved in a manner similar to UE uplink positioning, except that the RF wave angle of incidence cannot be determined from the above formula. Instead, a multilateration technique may be used to determine the LOB/AoA for each cell tower.

UE DL positioning implementations also employ reference signals. In the DL case, one approach for such network-based positioning may be to employ LTE cell-specific reference signals (CRS) as ranging signals. In addition, Position Reference Signals (PRS) introduced in LTE release 9 may be used. Thus, positioning may be done using CRS only, PRS only, or both CRS and PRS.

As with the UE uplink positioning embodiment, for the UE downlink positioning embodiment, a snapshot of the UE received signal in digital format may be sent to the LSU for processing. The UE may also obtain TA measurements and provide these measurements to the LSU. Optionally, the LSU may be provided by each serving sector from each serving cell tower (network node) according to TA measurements of the serving UE. As previously described, the LSU may determine each UE location relative to the serving cell tower and/or the neighboring cell towers, assuming that each serving cell tower and each neighboring cell tower location coordinates, the number of sectors per tower with each sector transmission mode azimuth and elevation, and each sector location at the tower are known. In an embodiment, the location determination may be performed in the UE or separated between the UE and the LSU or network node. In embodiments, all location determinations may be performed in the LSU or the network node, or split between the two.

The UE will transmit/receive measurement results and other information over the air using standard wireless protocols/interfaces. Information exchange between the LSU and the network nodes may occur over a wired network (e.g., LAN, WAN, etc.) using a proprietary interface and/or one or more standardized interfaces. The LSU may interface with the wireless network infrastructure using standardized interfaces and/or defined interfaces/APIs of network bearers. The location determination may also be split between the network node and the LSU or performed only in the network node.

For the UE Dl location implementation described above, the antenna port mapping information may also be used to determine location. The 3GPP TS 36.211LTE standard defines antenna ports for DL. A separate reference signal (pilot signal) is defined in the LTE standard for each antenna port. Thus, the DL signal also carries antenna port information. The information is included in a Physical Downlink Shared Channel (PDSCH). PDSCH uses the following antenna ports: 0; 0 and 1; 0, 1, 2 and 3; or 5. These logical antenna ports are assigned (mapped) to physical transmission antennas as shown in fig. 26. Therefore, the antenna port information can be used for antenna identification (antenna ID).

For example, antenna port mapping information may be used to determine hyperbolas (multi-point positioning) between RF wave incidence and the antenna (assuming that the antenna location is known). Depending on the location at which the location determination is performed, antenna mapping information must be available to the LSU or UE or network node. It should be noted that the antenna ports are indicated by placing CRS signals in different time slots and different resource elements. Each DL antenna port transmits only one CRS signal.

In case of Multiple Input Multiple Output (MIMO) deployment in an eNB or network node, the receiver is able to determine the time difference of arrival from a given UE. By knowing the receiver mapped (e.g., MIMO mapped) antennas, including antenna location, the RF wave incidence to the antenna (LOB/AoA) and hyperbola (multi-point positioning) for a given eNB antenna can also be determined. Also, at the UE, the UE receiver can determine the time difference of arrival from two or more enbs or network nodes and MIMO antennas. By knowing the eNB antenna locations and antenna mappings, it will be possible to determine the RF wave incidence (LOB/AoA) and hyperbola (multi-point positioning) from a given eNB antenna. Depending on the location at which the location determination is performed, antenna mapping information must be available to the LSU or UE or network node.

Other configurations of subsets of MIMO exist, such as Single Input Multiple Output (SIMO), Single Output Multiple Input (SOMI), Single Input Single Output (SISO), and so forth. All of these configurations may be defined/determined by antenna port mapping and/or MIMO antenna mapping information for positioning purposes.

In one aspect, the present embodiments relate to methods and systems for RF-based identification, tracking, and localization of objects including RTLS. According to one embodiment, the method and system employ geographically distributed clusters of receivers and/or transmitters that are precisely synchronized in time (e.g., within 10ns or better within each cluster), while inter-cluster time synchronization may be less accurate or not necessary at all. While a precise synchronization time of 10ns or more is described with respect to one particular embodiment, it is important to note that the predetermined synchronization time required to achieve an accurate position depends on the equipment utilized. For example, for some wireless system devices that require 3m accuracy for accurate position determination, the predetermined time may need to be 10ns or better, but for other wireless system devices, a position accuracy of 50m may be more than adequate. Thus, the predetermined time is based on the desired accuracy location of the wireless system. The disclosed methods and systems are a significant improvement over existing implementations of tracking and locating DL-OTDOA and U-TDOA technologies, which rely on geographically distributed independent (individual) transmitters and/or receivers.

For example, in DL-OTDOA techniques, the relative timing difference between signals from neighboring base stations (enbs) is calculated, and the UE position can be estimated in a network with a UE (handheld terminal) with or without UE assistance, or in a UE (handheld terminal) with network assistance (SUPL-based only control plane or user plane) with or without network assistance. In DL-OTDOA, upon receiving signals from three or more base stations, a UE measures the relative timing difference between signals from a pair of base stations and generates a hyperbolic position Line (LOP). At least three reference points (base stations not belonging to a straight line) are required to define two hyperbolas. The location (position azimuth) of the UE is located at the intersection of the two hyperbolas (see fig. 11). The UE location fix is relative to the base station's RF transmitter (antenna) location. For example, when LPP (LTE positioning protocol, release 9) is used, DL-OTDOA positioning is UE-assisted and an evolved serving mobile positioning center (E-SMLC) is server-based.

U-TDOA techniques are similar to DL-OTDOA, but the U-TDOA effects are reversed. Here, the neighbor Location Management Unit (LMU) calculates the relative arrival time of the uplink signal from the UE (handheld terminal) and can estimate the UE location in the network without UE assistance. Thus, U-TDOA is LMU-assisted and an evolved serving mobile location center (E-SMLC) is server-based. Once the relative time of arrival values from three or more LMUs are available, the E-SMLC server of the network generates a hyperbolic position Line (LOP) and a position line (position fix) for the UE (see fig. 27). The UE location fix is relative to the LMU antenna location. In one aspect, unlike DL-OTDOA, in the case of U-TDOA, time synchronization of the enbs (of the base stations) is not necessary, and only LMUs will require precise time synchronization for positioning purposes. For example, LMUs are basically receivers with computing capabilities. As another example, LMU receivers employ Software Defined Radio (SDR) technology. In another example, the LMUs may be receive-only small cell, macro cell, or dedicated small cell type devices.

Regardless of the implementation, associating the location of the SRS for a particular UE, as provided by the network, will enable the UE to be identified and located. The location of the SRS may be done at the network level or within a local sector such as a building DAS, a small cell, or a combination of small and macro cells serving a particular area. This solution may be able to correlate the location of the UE by coverage area if the location of the SRS for the UE is not known a priori. Doing so will show the location history that the UE has traveled. In some cases, it may be desirable to determine the location of a UE even if the network does not provide an indication of where the SRS for a particular UE is located. The location of the UE may be correlated with the SRS by determining the location or proximity of the UE to a known point, thereby correlating the UE with the SRS it is transmitting. Such location may be implemented through other location/proximity solutions, such as Wi-Fi and bluetooth. Users may also identify their location via a UE application or by walking to a predetermined determined location in order to identify their UE to a location solution.

In fig. 11 and 27, only the macro base station is shown. In addition, figure 27 shows an LMU co-located with a base station. These descriptions are valid options, but the LTE standard does not specify where LMUs can be placed, as long as the LMU placement meets the multi-point/three-point positioning requirements.

In one aspect, a common deployment of indoor environments is Distributed Antenna Systems (DAS) and/or small cells, which are inexpensive base stations that are highly integrated with RF. LMUs may also be placed in indoor and/or campus-type environments, e.g., U-TDOA may be used for DAS and/or small cell environments. In another aspect, accurate indoor location based on U-TDOA may be achieved with a combination of LMUs located indoors and macrocells located outdoors or with a smaller number of small cells, e.g., without requiring deployment of a DAS and/or small cells. Thus, LMUs may be deployed with or without DAS and/or small cells. In another aspect, LMUs may be placed in an environment using cellular signal amplifiers/boosters with or without DAS and/or small cells.

LTE release 11 also contemplates the integration of LMUs and enbs into a single cell. However, if the individual small cell enbs are geographically distributed, this would place an additional burden on the time synchronization requirements between small cells that the wireless/cellular service provider is not ready to meet, especially indoors and/or in other GPS/GNSS denied environments.

DAS systems are inherently synchronized to a much higher degree (accuracy) than geographically distributed macro/mini/small cells/LMUs. Using a DL-DTOA solution in a DAS environment would alleviate the time synchronization problem, but in a DAS environment a single base station serves a large number of distributed antennas, such that multiple antennas transmit the same downlink signal with the same cell ID (identification number). Therefore, the conventional DL-OTDOA method fails because there is no identifiable neighboring cell (antenna) that generates signals with different IDs. However, DL-OTDOA techniques may be used when employing multipath MITIGATION processors and multipath MITIGATION techniques/algorithms (as described in U.S. patent No. 7872583), or when extending the use of location consistency algorithms (as described in U.S. non-provisional application No. 13/566,993, filed on 3.8.2012 entitled "MULTI-PATH MITIGATION IN RANGEFINDING AND TRACKING on systems USING repeated identification RF TECHNOLOGY"); these patents are incorporated by reference herein in their entirety. However, these consistency algorithms have a limit to the number of antennas that transmit signals with the same ID. One solution is to reduce the number of antennas transmitting the same ID, e.g., to separate a large number of DAS antennas into two or more time synchronized clusters with different IDs. Such an arrangement would increase system cost (increase the number of base stations) and require the handheld terminal/UE to support the above-described techniques.

Employing U-TDOA in a DAS environment will also increase costs relative to adding/installing LMU units. However, no changes will need to be made to the UE (handheld terminal); only the base station software must be upgraded to support the U-TDOA functionality. Additionally, multiple LMUs may be integrated with (into) the DAS system. Thus, there are many advantages to using the U-TDOA method with LMUs when utilized in indoor, campus environments, and in other GPS/GNSS challenging, geographically limited environments.

Precise time synchronization between multiple base stations and/or small cells and/or LMUs geographically distributed in indoor and other GPS/GNSS-denied environments is more complex than time synchronization of macro cells and/or LMU devices used in outdoor macro cells (e.g., GPS/GNSS-friendly environments). This is because the macro cells in an outdoor environment have antennas that are elevated and open. Thus, the GPS/GNSS signal quality is very good and the macrocell antenna transmissions and/or LMU receivers can be synchronized with very high accuracy (standard deviation 10ns) over a sufficiently large area using GPS/GNSS.

In one aspect, time synchronization between multiple distributed base stations and/or small cells/LMUs is achieved for indoor and other GPS/GNSS denied environments by using an external synchronization source that generates synchronization signals that are shared by many base stations and/or small cells and/or LMUs. The synchronization signal may be derived from a GPS/GNSS, e.g. a 1PPS signal, and/or internet/ethernet networking, e.g. PTP or NTP, etc. The latter is a low cost solution but it does not provide the time synchronization accuracy required for accurate position, the GPS/GNSS derived external synchronization signals are more accurate-standard deviation as low as 20ns, but requires additional hardware and installation requirements, such as wiring these signals, and is therefore more complex/expensive. In addition, base station and/or small cell hardware/low level firmware may need to be changed to accommodate the external synchronization signal with greater accuracy. Furthermore, a standard deviation of 20ns is not accurate enough to meet the requirement of 3 meters, for example a standard deviation of about 10 ns.

To overcome the above limitations, one embodiment uses an LMU device 2800 with multiple receiveantennas 2802 andsignal channels 2804 as shown in the multi-channel LMU high level block diagram of figure 28. For example, one ormore signal channels 2804 may include signal processing components such as an RFE (RF front end) 2806, anRF downconverter 2808, and/or anuplink positioning processor 2810. Other components and configurations may be used. In one aspect, thesignal channels 2804 are co-located within the LMU devices 2800 and are closely time synchronized (e.g., about 3ns to about 10ns standard deviation). As another example,antennas 2802 from eachLMU signal channel 2804 are geographically distributed (e.g., similar to a DAS). As another example, an external time synchronization component (e.g., GPS/GNSS, internet/ethernet, etc.) may communicate with the LMU device 2800. Accurate time synchronization is easier to achieve within a device (e.g., LMU device 2800) than attempting to closely synchronize multiple geographically distributed devices.

For example, when two or more multi-channel LMUs (e.g., LMU devices 2800) are deployed, time synchronization between these LMUs may be relaxed so that multiple distributed multi-channel LMUs may be synchronized (using external source signals) using a low cost and low complexity approach. For example, internet/ethernet networking synchronization may be used, or common sensors (devices) may be deployed to provide timing synchronization between different multi-channel LMUs.

On the other hand, the multi-channel LMU approach reduces the number of hyperbolic position Lines (LOPs) that can be used to determine position fixes, but the improved time synchronization overcomes this drawback (see explanations and examples below).

When using the multi-point positioning/three-point positioning method, the UE positioning accuracy is due to the following two factorsFunction of element: geometric dilution of precision (GDOP) due to the geometric arrangement of macrocell towers/smallcells/LMUs, and a single ranging σ_{R_pseudo}The accuracy of the measurement (see hunter Seeber, Satellite geodety, 2003):

σ_POS＝GDOP×σ_{R_pseudo}

GDOP is a function of the geographical distribution of the transmitting antennas (in the case of DL-OTDOA) or the receiving antennas (in the case of U-TDOA). In the case OF regularly placed antennas, the two-dimensional GDOP estimate is equal to 2/√ N (h, b.lee, accuray ionization OF hydrogenolic multiple SYSTEMS, 1973); where in the case of a cellular network, N is the number of transmitters (macro cell towers/small cells/DAS antennas) that the UE can "listen to" (in the case of DL-OTDOA) or the number of LMU/LMU receive channels (in the case of U-TDOA) that can "listen to" the UE uplink transmissions. Thus, the standard deviation of the UE position error may be calculated as follows:

it is assumed that eight geographically distributed (indoor) single receive channel LMUs (periodically placed) are detecting UE uplink transmissions and that these LMUs are synchronized via a 1PPS signal (e.g., 20ns standard deviation). In this case, N-8, and there will be seven independent LOPs available for the UE location fix. Also assume that the standard deviation of therange error σ_R3 meters (about 10 ns); the accuracy of a single ranging measurement is then:

wherein sigma_SYNCIs the external time synchronization signal standard deviation (20 ns).

In this case (N ═ 8), a single ranging measurement of the UE position error and the standard deviation σ_POSEqual to 4.74 meters.

For example, if there are two, four receive channels LM with distributed antennas placed periodicallyU (e.g., multi-channel LMU device 2800) are detecting UE uplink transmissions, then each LMU will generate a set of three tightly time synchronized LOPs (e.g., about 3ns standard deviation); and for three independent LOPs, N-4. In this case, two UE position fixes are generated, each with a standard deviation error σ of 3.12 meters_POS. Combining these two position fixes by averaging and/or other means/methods will further reduce the UE position fix error. One estimate is that the error reduction is proportional to the square root of the number of UE position fixes. In this disclosure, this number is equal to 2, and the final UE position fix error σ_{POS_FINAL}2.21 m; the method comprises the following steps: 3.12/√ 2.

In one aspect, several multi-channel LMUs (e.g., LMU devices 2800) with relaxed synchronization between these multi-channel LMUs may be used in indoor and other GPS/GNSS denied environments. For example, within a multi-channel LMU device, the LMUs may be closely synchronized (e.g., a standard deviation between about 3ns and about 10 ns). Another embodiment makes use of the fact that: a plurality of single channel small cells/LMUs and/or small cells with integrated LMU equipment electronics (LMU functionality embedded in an eNB) may be clustered (e.g., integrated, co-located, etc.) in a rack enclosure (fig. 31, 32, and 33) and/or a cabinet (e.g., a 19 inch rack). Each single channel device antenna may be geographically distributed, as in a DAS. Devices within a cluster may be tightly time synchronized (e.g., less than or equal to 10ns standard deviation). Depending on the communication requirements, e.g. VoLTE, multiple rack enclosures may be synchronized, whereby a less costly and less complex approach may be used. Accurate (tight) time synchronization between multiple devices clustered (integrated) within a rack enclosure/cabinet is easier to achieve than if multiple geographically distributed devices were tightly time synchronized.

In another aspect, multiple LMUs may be integrated with (into) the DAS system, as shown in fig. 34. For example, the LMU receivers may share the received signals generated by each DAS antenna (e.g., share DAS antennas). The actual distribution of these received signals depends on the DAS implementation: active DAS and passive DAS. However, LMU and DAS integration implementations require sharing one or more receive signals generated by each DAS antenna with the LMU receiver channel and creating an almanac that matches (correlates) each DAS antenna coordinates to the corresponding LMU/LMU receiver channel. Also, clustering methods and/or the use of multi-channel LMUs are preferred ways for LMU and DAS integration.

In addition, in a similar manner, the received signals generated by each small cell antenna may be shared with LMU receiver channels. This may relax the time synchronization of the small cells, e.g. the positioning requirements do not need to be met, while the LMU/LMU channels will need precise time synchronization. Clustering methods and/or the use of multi-channel LMUs are preferred ways for such selected LMUs.

Integrating the LMUs and enbs into a single unit has cost advantages over the combination of separate eNB and LMU equipment. However, unlike the integrated LMU and eNB receivers, the stand-alone LMU receive channel does not have to process the data payload from the UE. Furthermore, because the UE uplink ranging signals (SRS, in the case of LTE, sounding reference signals) are repeatable and time synchronized (to the serving cell), each independent LMU receive channel may support (time multiplexed with) two or more antennas, e.g., serving two or more small cells. This in turn can reduce the number of LMUs (in small cell/DAS and/or other U-TDOA location environments) and reduce the cost of the system (see also fig. 28).

If the wireless/cellular network E-SMLC server lacks the functionality required for DL-OTDOA and/or U-TDOA techniques, that functionality may be performed by a location server that may communicate with the UE and/or LMU and the wireless/cellular network infrastructure and/or location service server (see fig. 29 and 30). Other configurations may be used.

In another aspect, one or more LMU devices (e.g., LMU 2802) may be deployed with a WiFi infrastructure, for example, as shown in fig. 35. Alternatively, the listening device may be used to monitor the LMU antenna in the same manner as the WiFi infrastructure. Accordingly, the LMU devices and/or channel antennas serving the LMUs may be co-located with one or more WiFi/listening devices 3500, such as one or more WiFi Access Points (APs). For example,WiFi devices 3500 may be geographically distributed.

In one embodiment, theWiFi device 3500 may be connected to a power source. An RF analog portion 3502 (e.g., circuitry) of one or more LMU devices or channels may be integrated with the LMU antenna such that theRF analog portion 3502 may share a power supply with the WiFi device 3500 (see fig. 35). For example, theRF analog portion 3502 of the LMU device or channel may be connected via a cable to an uplink positioning processor circuit (e.g., uplink positioning processor 2810), which may include baseband signal processing. As another example, such an embodiment facilitates improving a signal-to-noise ratio (SNR) because there may be signal amplification between the antenna and the interconnection cable between theRF analog portion 3502 and the baseband circuitry. Further,RF analog portion 3502 may down-convert the received signal (e.g., to baseband) and may relax cable requirements because the baseband signal frequency is a few magnitudes less than the signal received in the antenna. This relaxation of cable requirements may translate into a cost reduction of the connection and may significantly increase the transmission distance.

It should be understood that the ranging signals are not limited to SRS, and other reference signals may be utilized, including MIMO, cell-specific reference signals (CRS), and so forth.

In another embodiment, the 5G network-centric location approach may be improved by fusing the downlink OTDOA and uplink TDOA approaches. For example, in a macrocell environment, the uplink TDOA method lacks range. This is because the power of the uplink signaling of the UE is several orders of magnitude lower than the corresponding downlink signaling of the macro cell. Therefore, the probability that the UE signal is detected by the neighboring cell is low. For uplink TDOA, the 3GPP standard envisages deployment of LMUs to help improve the detectability of uplink signals (i.e. LMUs are basically uplink transmission receivers with additional signal processing capabilities). The deployment of many LMUs addresses the uplink signal detectability problem and enables multipoint positioning for locating UEs, but there are significant costs associated with deploying and maintaining a large number of such devices. Thus, the wireless network carrier does not deploy LMUs in an LTE 4G environment.

Therefore, in order to achieve uplink positioning in 4G and 5G environments, it may be necessary to utilize serving cell antenna system characteristics (attributes) of different methods for determining the UE location. For example, an uplink method that estimates the direction of arrival (angle) of the UE reference signal transmission (DoA/AoA) in the horizontal (also referred to as azimuth) plane may be used, depending on the serving cell antenna system design. Theoretically, the DoA/AoA in the vertical (also called height) plane can also be estimated. By combining the DoA/AoA calculations in both the horizontal and vertical planes, the UE can be located. However, in practice, the accuracy of the DoA/AoA estimation in the vertical plane is too low for accurate position determination due to the macrocell antenna design, or the DoA/AoA estimation in the vertical plane cannot be done at all because the antenna elements in the vertical plane cannot be accessed.

In order to obtain a position fix using the uplink DoA/AOA method, it may therefore be necessary to estimate the distance from the UE to the serving cell. The distance may be derived from Round Trip Time (RTT) and Time Advance (TA) information, which is based on measurements performed by the serving cell. However, these measurements may lack the required accuracy due to radio wave propagation phenomena. One possible way to solve the accuracy problem is to use downlink OTDOA to estimate the distance between the UE and the serving cell. A drawback of this solution is that downlink OTDOA requires accurate synchronization between the serving cell and the neighboring cells for UE position determination, e.g. the positioning accuracy is affected by synchronization errors. Uplink TDOA in macro environments is also affected the same, so this is not an alternative.

However, there is no such error for uplink DoA/AoA, since only the serving cell is used, i.e. no neighboring cells are used, and thus no synchronization is required. In contrast, when downlink OTDOA is used to determine the distance between the serving cell and the UE, the measurement result may include the above-described synchronization error, but the error may be suppressed if downlink OTDOA is used only to determine the initial UE position. Thereafter, the UE position can be calculated based on UE DoA/AoA and UE velocity (estimated from the same reference signals as DoA/AoA and/or other reference signals) measurements by state-of-the-art tracking algorithms as long as the same serving cell is used. Over time (i.e., multiple measurements), the tracking algorithm or tracker will reduce the effects of synchronization errors. It should be noted that 4G LTE does not support soft/softer handover between serving cells, so the initial UE distance to serving cell measurement may have to be repeated each time the UE is handed over from one serving cell to another.

The tracking algorithm may be based on radar and sonar performance enhancement strategies. A tracking algorithm or tracker may provide the ability to predict future positions of multiple moving objects based on the location history and velocity of the individual objects as reported by the sensor system. There are many different types of trackers, including particle filter algorithms, kalman algorithms, and the like. The use of a tracker may also improve UE positioning when calculating the initial UE position based on RTT measurements as described above. The choice of whether to use downlink OTDOA or RTT/TA to measure the distance from the UE to the serving cell depends on the amount of error in the distance estimate. The larger the error, the more the performance of the tracker may be affected. In addition, after the handover, a certain time is required for the network to estimate the RTT. During this time, the reported RTT measurements may be invalid. In one embodiment, two different independent trackers are utilized simultaneously, and the position of the faster converging tracker is used (i.e., the position is determined first). Yet another embodiment is to estimate the distance for each position measurement using downlink OTDOA and estimate the distance and correct the synchronization error using the tracker. This method yields the best accuracy.

As noted above, the uplink approach is inherently network-centric in that the relevant UE reference signal transmissions may be collected and pre-processed by the eNodeB and/or LMU integrated with the eNodeB in the macro environment or stand-alone LMU, LMUs integrated with the DAS system in other environments, etc., and then forwarded to the LSU for further processing and UE location determination using one or more network protocols. In the case of downlink OTDOA, the task of collecting and pre-processing downlink reference signals is performed by the UE. The UE then transmits the collected downlink data to the LSU. The UE uses the control plane and/or LTE user (data) plane to handle communications with the LSU. Thus, the signaling may conform to the OMA Secure User Plane Location (SUPL) protocol and/or 3GPP, such as the LTE Positioning Protocol (LPP).

Many uplink and downlink reference signals may be used for UE position determination. The most commonly used for uplink positioning includes Sounding Reference Signals (SRS) and/or demodulation reference signals (DMRS). The most commonly used for downlink positioning includes Positioning Reference Signals (PRS) and/or cell-specific reference signals (CRS). The reference signal may be collected in a digital format (i.e., samples) and may be pre-processed before being sent to the LSU. The digital samples of the reference signal may be extracted from baseband I/Q samples in the time domain or baseband I/Q samples in the frequency domain and Resource Elements (REs) from the OFDM symbol. The resource elements are complex-valued coefficients representing one OFDM subcarrier within one OFDM symbol duration. Thus, the REs may represent LTE symbols in the frequency domain. The I/Q values represent the in-phase (I) and quadrature (Q) components of the signal. Thus, the reference signal may be represented by I/Q samples in the time domain or I/Q samples in the frequency domain or REs containing symbols of the reference signal.

In downlink OTDOA UE implementations, both PRS and CRS reference signals may be collected. In other words, the method may utilize PRS, CRS, or both types of signals. Such a hybrid mode of operation (i.e., PRS or CRS or both PRS and CRS) has the advantage of allowing the network operator to dynamically select an operating mode depending on the situation or certain network parameters. For example, PRS may have better listening than CRS, but using PRS may result in reduced data throughput. CRS does not affect throughput and has a higher reference signal density, which is advantageous when the UE is moving. Furthermore, CRS is backward compatible with all previous LTE releases (e.g.,release 8 and lower). Thus, the hybrid approach provides the network operator with the ability to trade off or balance between snooping, throughput, compatibility, and accurate positioning/tracking of moving targets.

In one embodiment of downlink OTDOA, the UE receiver may include a detector configured to detect a plurality of OFDM symbols carrying reference signals (e.g., PRS and/or CRS) and isolate the plurality of OFDM symbols from one or more downlink frames. The detector may be further configured to extract Resource Elements (REs) from the OFDM symbols on a per symbol basis (see fig. 9), and may be further configured to collect and store the Resource Elements (REs) from a plurality of OFDM symbols on a per symbol basis, i.e., to generate, for each symbol Identification (ID), a downlink data structure including the REs associated with the symbol ID. Further, the detector may be configured to collect downlink metadata, including each frame start and other related information and/or side information.

In one embodiment, an exemplary data structure for an OFDM symbol with CRS RE elements is as follows:

-CRSR data structure: [ number of radio slots captured in one acquisition block, time interval between acquisition blocks (number of slots), CRS data length, and CRS data (resource elements) ].

In one embodiment, exemplary metadata transmitted from the UE to the LSU includes:

-serving cell information: physical cell Id, Global cell IdEUTRA-and UTRA, earfcn-DL, System frame number, time Slot number, UTC timestamp, rsrp-result, rsrq-result, UtID, Ut,

ue-RxTxTimeDiff, uplink path loss, bandwidth ] in a Physical Resource Block (PRB).

Neighbor cell information (0 to 32 neighbor cells): [ physical cell Id, Global cell IdEUTRA-and UTRA, earfcn-DL, System frame number, rsrp-result, rsrq-result.

-UE information: [ UE ID, UE category, mobility state, mobility history report ].

In one embodiment of downlink OTDOA, the UE receiver may comprise a communication processor configured to signal the LSU and exchange downlink data with the LSU, including being configured to send RE data and metadata to the LSU and receive command and assistance information. It should be noted that the signaling may conform to the OMA SUPL protocol and/or the 3GPP LPP, or a combination of LPP and SUPL. Further, proprietary interfaces and/or protocols may also be utilized.

In one embodiment of downlink OTDOA, the detector of the UE receiver may be configured to extract reference signal I/Q samples in the time domain from OFDM symbols on a per symbol basis, and further configured to collect these I/Q samples in the time domain from a plurality of OFDM symbols on a per symbol basis, i.e. to generate for each symbol ID a downlink data structure comprising I/Q samples in the time domain associated with that symbol ID. Further, the detector may be configured to collect downlink metadata, including each frame start and other related information and/or side information.

In one embodiment of downlink OTDOA, the detector of the UE receiver may be configured to collect I/Q samples in the frequency domain from a plurality of OFDM symbols on a per symbol basis, i.e. to generate a downlink data structure for each symbol ID, the downlink data structure comprising I/Q samples in the frequency domain associated with that symbol ID. Further, the detector may be configured to collect downlink metadata, including each frame start and other related information and/or side information.

In one embodiment of downlink OTDOA, the detector of the UE receiver may be configured to collect REs from multiple OFDM symbols on a per symbol basis, i.e. to generate a downlink data structure for each symbol ID, the downlink data structure comprising REs associated with the symbol ID. Further, the detector may be configured to collect downlink metadata, including each frame start and other related information and/or side information.

The OFDM symbols carrying the reference signal RE may include both the payload signal RE and the reference signal RE. Thus, the REs collected by the detector from the plurality of OFDM symbols, and thus the downlink data structure generated from these collection procedures, include both payload signals RE and reference signals RE. When this data is sent to the LSU, the payload RE becomes overhead, thereby reducing the uplink capacity.

The positions of CRS and PRS reference signals RE in the frequency dimension of a symbol may be determined by the cell ID; configuring an antenna; an antenna port; the number of time slots within a radio frame and the number of OFDM symbols within a time slot (see also 3GPP 36.211 version 13 or ETSI TS136211 version 13.0.0). If this information is known to the detector of the UE receiver, the detector may be configured to remove the payload REs, thereby reducing the size of the downlink data transmitted to the LSU, i.e., reducing overhead. The amount of reduction may vary depending on the reference signal type, neighboring cell ID, and other cell parameters. For example, for CRS, the data size reduction may be 3 times (for cells with dual antenna sectors). In other cases, there may not be a measurable reduction, but this may be quite rare, since the reference signal RE position in the frequency dimension of the symbol repeats on a [ (cell ID) mod6] basis. Thus, for CRS, the average amount of reduction in data size relative to the worst case scenario may be about 40%.

Further data size reduction may be obtained by compressing the complex RE magnitude and phase into a smaller number of bits. There are many compression algorithms. Some compression algorithms are well known, such as A-law and u-law compliance algorithms. Other algorithms address baseband unit (BTU) pool and Remote Radio Unit (RRU) connection bandwidth reduction for C-RAN (centralized, cooperative, cloud radio access network) architectures. Note that: in the C-RAN architecture, the baseband unit (BTU) is centrally located in a pool connected to a Remote Radio Unit (RRU) via optical fiber. Furthermore, compression algorithms exist for radar technology.

In one embodiment, the detector of the UE receiver is configured to compress the RE size of 16 bits to 32 bits to an RE size of 8 bits, e.g., 2 to 4 times the data size reduction.

In one embodiment of downlink OTDOA, the LSU is configured to process downlink reference signal data and other relevant and/or auxiliary downlink information, including frame start, transmitted from one or more UEs. The process includes searching for all detectable (meeting certain criteria) reference signal transmitters from the location database/list. In addition, processing is performed for each antenna (e.g., antenna port of the LTE device and/or related LTE network components). The output of the LSU is the location of one or more UEs and other downlink location related metadata (such as confidence radius values, FCC NG911 location accuracy measures, etc.).

The LSU may include a downlink signal processor configured to estimate time of arrival (ToA/TDOA) and/or time of flight (ToF) of reference signals from downlink reference signal data and other information transmitted by the UE. The processor is also configured to determine a time difference (i.e., RSTD/TDOA, where RSTD means reference signal time difference) between the reference cell and the neighboring cell (measurement cell). The downlink signal processor may include positioning signal processing algorithms and other techniques and technologies, including multipath mitigation algorithms and methods, including advanced spectrum estimation algorithms, Constant False Alarm Rate (CFAR) detection algorithms, space-time adaptive processing (STAP), and the like. Further, the downlink signal processor may be configured to estimate Carrier Frequency Offset (CFO) with one or more specific algorithms and/or techniques, which may allow the LSU to track one or more mobile UEs and suppress clock frequency mismatch between the transmitter (cell) and the receiver (UE). The CFO estimation is used to update (correct) the downlink reference signal data.

Most cell towers employ MIMO antennas. Each cell tower sector has a MIMO subsystem consisting of multiple (two or more) antennas. These antennas are fully coherent, i.e. time and phase synchronized. To avoid interference between reference signal transmissions: a) the gold code seed (used to encode/generate the reference signal) is different for each antenna, b) different resource elements (subcarriers) are allocated for each antenna reference signal transmission, and c) when one antenna from the sector MIMO subsystem is transmitting the reference signal, the transmissions from the other antennas are muted. Thus, the UE receiver is able to detect (distinguish) the reference signal transmissions from each sector subsystem antenna.

The most common today is a sector antenna subsystem with dual antennas; and as with any MIMO antenna subsystem, the antennas are spatially separated (about 6 feet). However, it should be understood that the sector MIMO subsystem is not limited to two antennas and that the antenna separation distance may vary. A conventional way to select reference signals from different antennas of the same sector is to select the antenna (signal) with the highest SNR (signal-to-noise ratio) and/or SNIR (signal-to-noise + interference ratio). However, in many cases, these standards do not guarantee that a direct line of sight (DLOS) or direct path of the reference signal is detected, and the UE position fix will be determined by the reflected signal. In this case, i.e. no DLOS/direct path is detected, the positioning accuracy is affected. Therefore, for accurate positioning, Detection (DLOS) or direct path signals are required.

Because the wireless network is a terrestrial system, the DLOS path is often obstructed to varying degrees, and the DLOS signal strength can often be significantly (15dB or higher) below the reflected signal strength. Meanwhile, the wireless network is a terrestrial system, and even if DLOS is severely hampered (>15dB), there is always a direct path for the RF signal from the sector antenna to the UE receiver due to RF propagation phenomena (surface waves, fresnel waves, etc.). The direct path is slightly longer than the DLOS path, but closer to the DLOS path length than the reflected path, i.e. with minimal impact on the position determination accuracy.

The sector antenna spatial separation is at least two orders of magnitude smaller than the distance from the UE to the sector antenna. Therefore, each antenna signal propagation path will be very close and should experience the same attenuation. However, the sector antenna spatial separation is large enough that the reference signal from each sector antenna experiences multipath phenomena that will affect DLOS/direct path signal strength as well as reflected path signal strength. Multipath interference can be constructive or destructive, i.e., amplifying or attenuating the signal; and due to the antennas of the sectors, the spatially separated signals from each antenna will be affected differently, i.e. the effects are antenna dependent.

When comparing signals from two antennas of the same sector, the reference signal from the first antenna may have been amplified while the DLOS/direct path signal is attenuated, e.g., the reflected signal power may be significantly higher (>20dB) than the DLOS/direct path signal power. The reference signal from the second antenna may have a higher DLOS/direct path signal strength (e.g., 3dB or 4dB) and a reduced reflected signal strength (e.g., 5dB reduction) compared to the first antenna. At the same time, the DLOS/direct path signal power is much lower (perhaps > -10 dB) than the reflected signal power for both antennas. Thus, for example, a signal from the first antenna with greater reflected signal power will have a higher SNR/SNIR and will be selected (using conventional methods of selecting reference signals from antennas) for determining the UE position fix. However, the first antenna DLOS/direct path signal power may drop below the detection threshold, thereby affecting the positioning accuracy. Then, increasing the probability of DLOS/direct path detection is to select a signal from the second antenna with a higher DLOS/direct path signal strength (3dB to 4 dB).

In one embodiment of the LSU downlink signal processor, the ToA/ToF results for each antenna of a given sector are evaluated (compared) to determine DLOS/direct path. By definition, DLOS/direct path means the shortest distance between the UE and the tower relative to the reflected path. Thus, the DLOS/direct path will have the earliest ToA or the least ToF. Based on the above description, the following are possible results when comparing ToA/TOF results from a pair of antennas:

1. signals from multiple antennas produce the same earliest ToA value and/or minimum ToF value.

2. The earliest signal ToA and/or minimum ToF value from one antenna is less than the earliest ToA and/or minimum ToF value from another antenna.

In the first case, the antenna (signal) selection is based on a confidence measure (see below). In the second case, the antenna (signal) with the lower ToA/ToF value is used to calculate the position fix, provided that the signal meets the confidence measure parameter threshold requirement. Note that: the confidence measure qualifier is needed to avoid (suppress) false alarms, such as false DLOS/direct path detection (decision) caused by noise and/or interference exceeding the detection threshold.

If the cell tower sector antenna subsystem consists of three or more antennas, an iterative process is employed whereby: a) instep #1, a plurality of antenna pairs are formed and evaluated, b) instep #2, antenna pairs are formed from the remaining subset of antennas and each antenna pair is evaluated, and c)step #2 is repeated until no pairs can be formed, i.e. only one antenna (signal) is left (available).

Further, the downlink signal processor is configured to calculate a TOA confidence measure for each TOA/ToF from each cell and each cell sector antenna. The calculation may include whether the overall signal strength and/or SNR/SNIR of the received signal meets a desired threshold as well as ToA/ToF signal statistics, such as standard deviation, Mean Absolute Deviation (MAD), and the like. If a direct path is identified, the calculation may also include whether a direct path/DLOS is found and the SNR/SNIR of the direct path/DLOS. The additional information may include whether the serving cell is the closest cell and whether the serving cell has the highest SNR/SNIR, and a geometric dilution of precision (GDOP) calculation for each RSTD combination. It should be noted that GDOP depends on the geometry of the cell location and the antenna subsystem directivity of the sector (in the azimuth plane). The GDOP may indicate how the geometry will affect the final UE position estimate. The GDOP value may depend on the angle at which two given RSTD/TDOA lines intersect. In the best case (where GDOP ═ 1), the angle is 90 degrees. In the worst case (where GDOP >20), the angle is small. The RSTD/TDOA hyperbola may also be referred to as a line of Location (LOP).

As in the case of a cell sector antenna subsystem, a UE may include multiple (two or more) antennas and associate the antennas with multiple receive channels. Any of these antennas may receive the reference signal, providing the UE with the option of collecting incoming signals from each UE antenna. According to the aforementioned downlink OTDOA UE embodiments, the reference signals from each antenna may be collected and pre-processed by the UE and then sent to the LSU. The Ue antennas may be closely spaced, which reduces multipath impact variations between antennas. However, the UE antenna may be designed for polarization diversity. Thus, the above antenna (signal) selection decision stream (algorithm) is extended by repeating the sector's antenna selection algorithm for each UE antenna and then selecting between the remaining candidates using the aforementioned sector's antenna selection algorithm.

Embodiments of the LSU may include a downlink signal processor that compares the results between the ToA/ToF results from each antenna of a given cell/sector. The downlink signal processor may also compare the results between each UE antenna as described above. The rationale for such comparisons is that antenna polarization phenomena can cause some interference and/or reflection path attenuation while amplifying the DLOS/direct path signal. In other words, the redundancy of information associated with this analog comparison may increase the probability of DLOS/direct path detection.

The downlink signal processor is further configured to estimate a Carrier Frequency Offset (CFO). There are two main reasons for CFO: the first reason is doppler shift, which is the result of relative motion between the transmitter (cell) and receiver (UE) that exists in a mobile environment, and the second reason is clock frequency mismatch between the transmitter (cell) and receiver (UE), which results in residual CFO at the UE receiver after the down-conversion process. CFO estimation is needed to preserve/preserve the orthogonality properties of the subcarriers because the loss of orthogonality will degrade the OFDM system communication performance. Similarly, when OFDM reference signals are used as ranging signals for UE positioning, i.e. to determine ToA/TDO and/or ToF time, the subcarrier frequency offset will affect the time of arrival (ToA/TDOA) and/or time of flight (ToF) estimation accuracy. Thus, to mitigate the effects of UE movement and/or clock frequency, the downlink signal processor performs CFO estimation that corrects the reference signal data to mitigate the effects of UE movement and/or clock frequency offset on ToA/TDOA and/or ToF estimation accuracy. It should be noted that when the UE receiver is synchronized with the serving cell and the CFOs are calculated relative to the serving cell, the CFOs from multiple neighboring cells for ToA/TDOA and/or ToF estimation for UE position determination need to be corrected. Here, the doppler shift will be different for each cell depending on the direction of movement of the handheld terminal relative to the cell location. In addition, the clock frequency mismatch between each cell and the receiver (UE) is cell dependent. Furthermore, for accurate positioning, these CFOs should be estimated with a higher accuracy than for communication purposes.

In LTE and other OFDM-based systems, the CFO can be estimated using either time domain or frequency domain methods. In the time domain, the time domain or frequency domain method is a commonly used Cyclic Prefix (CP) method and training sequence method. The frequency domain estimation method can be further classified into a training symbol method and a pilot method. Both the training sequence and training symbol approaches require dedicated training sequences or training symbols that are not present (transmitted) in the LTE frame or symbol structure. Adopting one of these methods would require changing the LTE frame/symbol format, which would affect implementation into existing mobile wireless networks. On the other hand, CP and pilot (also referred to as reference) signals are part of the LTE frame or symbol structure. The pilot-based approach yields more accurate CFO estimation than CP-based CFO estimation. Furthermore, time domain CP-based estimation cannot be used for embodiments where resource elements are collected by the UE and sent to the LSU to determine the position fix, since the RE data set does not include CP data. Thus, the present inventors' CFO estimation implementation utilizes multiple pilot (i.e., reference) signals in an lte (ofdm) frame.

In one embodiment of CFO estimation, the CFO is estimated in the frequency domain and the reference signal is compensated with the estimated CFO in the time domain. In this embodiment, the embodiment includes an FFT of all time slot reference signal subcarriers in the frequency domain. CFO estimation is a process of a CFO estimation process described below by searching for a peak in a two-dimensional space created in the frequency domain.

In this embodiment, a single LTE frame is used to determine the CFO. However, a single frame is not a limiting factor, as two or more frames and portions of a frame, such as 10 slots (half-frames), may be used. In an LTE frame, reference signals may not be present in every symbol, see CRS signal example in fig. 9. On the other hand, according to fig. 9, each LTE frame slot has the same distribution of CRS signals, so that CFO estimation can be performed on a slot basis.

The following is a description of a method of estimating CFO. This procedure is applied to the individual reference signals from each of the serving cell and the neighbor cells that can be listened to.

Step 1: the RE of the reference signal is demodulated using a matched filter in the frequency domain.

Step 2: when CRS is used, the demodulated CRS samples (at CRS subcarriers) in each slot are combined to produce multiple combined CRS signals on a per slot basis, i.e., 20 signals per frame of 0.5 millisecond duration (interval) or 10 millisecond duration. Note that: according to the above description, the combined CRS sample sequence in each slot is in the frequency domain.

And step 3: an Inverse Fast Fourier Transform (IFFT) is applied to each slot CRS (CRS subcarriers in the frequency domain) generated for each slot CRS sequence in the time domain. Thus, there will be 20 CRS sequences at the end of this step, each in the time domain. The number of elements in each CRS sequence (in the time domain) is the same and is equal to the duration of the CRS signal divided by the ADC sampling rate (in the time domain). Assuming that the number of elements in the CRS sequence is N, every N elements, where N belongs to 1, … …, N; a sequence of 20 elements may be formed. In such an "n" sequence, each element is from a different time slot.

And 4, step 4: a Fast Fourier Transform (FFT) is applied to every 20 element "N" sequences to yield a total of 20 element by N sequences in the frequency domain.

And 5: searching for a peak of an N × 20 element space in a frequency domain; and the CFO is calculated from the maximum value of the peak.

The peak maximum determination accuracy and hence the CFO estimation accuracy is limited by the time interval between the time slot and the frame duration. Therefore, to improve accuracy in this embodiment, an interpolation algorithm is employed when finding the maximum of the peak.

In another embodiment, instep 4, an advanced spectrum estimation algorithm (e.g., Matrix Pencil, music, ESPRIT, etc.) is substituted for the FFT operation. These algorithms enable further improvement of the maximum determination accuracy of the peaks.

It should also be understood that a) other reference signals may be processed in a similar manner, b) reference signals may be combined on a basis other than every slot, and c) reference signals may not need to be combined if available on a per symbol basis.

In LSU embodiments, the output (results) from the downlink signal processor (including ToA/ToF values, RSTD/TDOA values, confidence measures, DLOS/direct path probabilities, etc.) are communicated to a navigation (location) processor configured to estimate one or more UE locations and generate other downlink location-related metadata, such as confidence radius values, FCC NG911 location accuracy measures, etc.

For downlink OTDOA UE positioning implementations, the LSU may include a navigation processor that utilizes a multipoint positioning technique or method, also referred to as hyperbolic navigation. Hyperbolic navigation is based on timing differences, i.e., RSTD/TDOA, without reference to a common clock. The navigation processor may also be configured to take advantage of the information redundancy described above (e.g., TOA/TOF, confidence measure, DLOS/direct path probability, etc.) to mitigate ambiguity in the multi-point location orientations and to apply a location consistency algorithm.

Multipoint location techniques and methods require solving multiple hyperbolic (RSTD/TDOA) equations for which multiple different algorithms/methods can be used to find the correct solution. Some algorithms/methods involve iterative methods that may start with an initial estimate or "guess" of the target (UE) location. Each iteration may then be utilized by determining a local linear least squares position solution to improve the estimate. One drawback of this approach is that the initial position estimate is required to be very close to the final position solution in order to guarantee convergence and/or the absence of local minima, which can lead to significant position errors. On the other hand, this method may work well in the presence of an overdetermination of more measurement equations than unknowns. In this regard, it should be noted that overdetermining reduces the likelihood of ambiguous and/or irrelevant solutions that may occur when only a minimum required number of measurements are available.

Non-iterative solutions also exist for the hyperbolic position estimation problem. These solutions are closed and effective for both far and near sources, eliminating the convergence and/or local minimization problems of the iterative approach. One drawback of non-iterative solutions is that these solutions require a priori knowledge of the approximate location to be determined, such as the distance between the UE and the first cell (e.g., serving cell). Another disadvantage of non-iterative solutions is that these solutions are closed-form solutions that are not designed for overdetermined situations. However, a non-iterative solution can be made to work in an over-determined situation by converting the initial set of non-linear TDOA equations into another set of linear equations with additional variables. For example, a weighted linear least squares algorithm provides an initial position solution, and a second weighted least squares then provides an improved position/position estimate using known constraints of the source coordinates and additional variables.

In one embodiment, when an over-determination condition occurs, multiple sets of three RSTD/TDOA subsets are formed. A closed form solution for each subset is then found. The location fix is then completed using a location consistency algorithm.

In another embodiment, the location fix is found by a combination of iterative and non-iterative solutions from the same set of RSTD/TDOA values.

In light of the above, iterative and non-iterative methods need to have a tight initial estimate of the UE position. This estimate can be enhanced by time evolution (TADV or TA), also known as RTT, information. The timing advance is used to compensate for propagation delay as the signal travels between the UE and the serving cell tower. The serving cell base station allocates a TA to the UE based on its measured distance of the UE (see fig. 37).

In LTEtiming advance type 1, the measurement (see fig. 38) corresponds to the round trip time, i.e. the signal round trip propagation delay. However, the timing advance propagation delay may come from DLOS/direct path or reflected path and include propagation delay through cell tower cables and base station/UE electronics. Further, the UE is in 4xT_sAdjusts its transmission timing, where T_sIs the LTE system timing, equal to 32.55 ns.

Type 1 is defined as the sum of the receive-transmit time difference at the eNB and the receive-transmit time difference at the UE:

TADV ═ (eNB Rx-Tx time difference) + (UE Rx-Tx time difference).

Therefore, the distance d to the base station is estimated using:

d ═ c (TADV/2), where c is the speed of light; or

d c (RTT/2), where c is the speed of the light;

ta (rtt) may be obtained from the serving cell and represents an independent UE range estimate from the serving sector. However, the TA is not available from the UE. Instead, the UE provides access to the receive transmission time difference (i.e., UE Rx-Tx). According to the above UE Rx-Tx-RTT-ENB Rx-Tx. On the other hand, according to fig. 37 and 38, when adjusting the UE TA, the serving cell eNB Rx-Tx time difference will be the same for all UEs. Thus, the UE Rx-Tx measurements will still correspond to RTTs, but with offsets that will depend on the cell tower antenna cable length and base station electronics.

In one embodiment, the antenna cable length propagation delay may be estimated from the tower height, and the propagation delay of the base station electronics may be estimated from statistics collected from the individual towers.

If the RTT is known, the UE may be positioned along an arc defined by the serving sector's azimuth beam width and RTT/2 distance (also referred to as radius). Because the azimuth beamwidth of the serving sector may be a large value of up to 120 degrees, the length of the arc grows rapidly as the distance affects the initial UE position estimation accuracy. However, due to the fact that the UE can also be located by the intersection of the azimuthal beamwidths of the serving sector and the neighboring cell sectors, the accuracy of the position/location estimate can be improved. This approach helps to mitigate the effects of arc growth. Further improvements can be achieved by taking into account sector antenna mechanical and/or electrical downtilt angles, antenna gain, positive beam width (in addition to azimuth beam width), and cell tower height and tower structure type.

LTE does not support soft UE handover, but in many cases, a UE is switching between two or more neighboring cells even if the UE is stationary or quasi-stationary. In addition to signal propagation interference, the serving cell switch may also be a result of wireless network eNB load balancing efforts.

In one embodiment, these frequent serving cell handovers are used to estimate RTT values from two or more geographically different serving cells. As previously described, the UE may then be positioned along an arc defined by the azimuth beam width and RTT/2 distance of the serving sector. Thus, there will be two or more such arcs defined by two or more geographically distinct serving cells, and the UE location will be determined at the intersection of these arcs. It should be noted that there may be multiple intersections or no intersections at all due to inherent RTT estimation errors. However, redundant arc information further enhances the RTT-based position fix improvement described above, which is also used in this embodiment.

To develop an even finer initial UE position estimate, the LSU navigation processor may be configured to work with the LSU uplink signal processor. As with the LSU downlink signal processor, the LSU uplink signal processor may receive uplink reference signal (e.g., SRS and/or DMRS) data collected from one or more UEs and pre-processed by an eNodeB (cell). At the eNodeB, digital samples of the uplink reference signal are extracted from the baseband and collected as uplink reference signal data along with associated uplink metadata. The UE uplink data and associated uplink metadata are then sent to the LSU uplink signal processor. The uplink signal processor may be configured to determine the AoA/DoA observable based on the UE uplink data and related uplink metadata and known configurations/parameters of the eNodeB sector antenna array included in the uplink metadata. The AoA/DoA observable is then sent to the LSU navigation processor, which may reduce observable ambiguity and generate an AoA bearing (LOB) line and/or direction of arrival (DoA) from the AoA observable and uplink metadata.

In one embodiment, the AoA/DoA estimate generated by the LSU navigation processor will significantly limit the arc growth versus distance from the eNodeB (serving sector). This is because the error in AoA bearing (LOB) and/or direction of arrival (DoA) estimated by the LSU navigation processor is about one degree or less, unlike sector azimuth beamwidths that may reach 120 degrees. Thus, the arc growth can be reduced by a factor of 100 when utilizing the LSU uplink signal processor and the LSU navigation processor, as compared to the use of conventional sector azimuth beam width. Thus, the initial UE position estimate may be 100 times more accurate. Thus, the AoA/DoA estimation may improve downlink OTDOA UE positioning accuracy of the navigation processor, and this more accurate downlink OTDOA UE positioning may in turn improve uplink AoA/DoA/UE position fix of the navigation processor. This therefore enables embodiments with joint uplink/downlink or downlink/uplink UE positioning.

Wireless network environments sometimes refuse to use a multipoint positioning method that requires at least three reference points (for 2-D positioning) in order to obtain the UE position. When four or more fiducials are available, 3-D locations may also be extracted. Further, if the signal is received by the MIMO antenna, the orientation of the UE may be established. The height of the UE may also be determined in the presence of vertical snapshot information available from the MIMO antennas. For example, in some dense urban environments, wireless networks use only two high-power cell towers filled with large areas of RF signals. Although DLOS is not available in this environment, the impact of DLOS absence is small because for data communication purposes, the reflected signal can be used for communication as long as the reflected path delay is less than the Cyclic Prefix (CP) length. This presents a problem for relying on common navigation techniques where at least three reference points are detectable.

In one embodiment, a UE located in a two tower/cell environment is further illustrated in fig. 36. The method may begin by plotting the TDOA hyperbolas of the two cell/tower sectors 3602 and 3604 that are detectable, such ashyperbolas 3606 and 3608, and finding the hyperbolas to which the target UE belongs. The UE's hyperbola can be determined by finding which sector azimuth beam the UE also resides in from each of cells/towers 3602 and 3604. The hyperbolas belonging to the two sector azimuth beams are the selected hyperbolas. As shown in fig. 36, cell/tower 3602 has a sector azimuth beam-width 3610, while cell/tower 3604 has a sector azimuth beam-width 3612. In fig. 36, the exemplary sector azimuth beamwidth is the same, equal to 60 degrees. Anintersection 3616 betweensector azimuth beamwidth 3610 andsector azimuth beamwidth 3612 is the region of highest probability of the UE being located. Thehyperbola 3608 belonging to the region of highest probability is the selected hyperbola.

If TA or UE Rx-Tx (also referred to as RTT) measurements from at least one sector (tower) are available, the UE may then be positioned along an arc defined by the serving sector azimuth beam width and RTT/2 range (e.g., radius of the arc). Thus, the UE may be located near the intersection of the hyperbola and the arc. Note that: there may be multiple intersections or no intersections at all due to inherent RTT estimation errors. However, redundant arc information further enhances RTT-based location fixes.

The UE belongs to the LOB and hyperbola intersection if AoA/DoA estimates from at least one sector (tower) are available. This solution is probably the most accurate.

However, when neither RTT nor AoA/DoA estimates are available, then the UE location fix may be determined using one or more heuristic methods, for example by scoring the intersection points corresponding to each cell/tower (sector) on the selectedhyperbola 3608. The score may be based in part on the cosine of the angular difference between the direction in which the cell/tower is pointing and the direction of the point on the hyperbola tested from the cell/tower sector. As shown in fig. 36, cell/tower 3602 points indirection 3620 and cell/tower 3604 points indirection 3622 definingintersection points 3630 and 3634. The remainder of the score may come from the distance from eachpoint 3630 and 3634 to the corresponding cell/tower. Once the score for eachpoint 3630 and 3634 has been determined, the point with the highest score from cell/tower 3602 and cell/tower 3604 may be determined. The two best scoring points on the hyperbola are then weighted according to the SNR of their corresponding cell/tower. Finally, a point between the two best scoring points (closer to the higher weighted point by SNR) is chosen as the UE location fix. As shown in fig. 36, when the true location of the UE islocation 3652, this results in an estimatedUE location 3650.

Returning to the discussion of the LSU embodiments, the LSU may also include a communications processor configured for signaling exchanges and information exchanges with the UE, eNodeB, and network elements. The signaling may conform to the OMA SUPL protocol and/or 3GPP LPP/LPPa, or a combination of LPP, LPPa, and SUPL, as well as other protocols used or available for communication with the network, such as the LCS-AP protocol. In addition, proprietary interfaces and/or protocols may also be utilized.

Examples of information so exchanged may be as follows:

-site name:

■ technology (i.e., 4G, 5G), active (e.g., Y/N) (i.e., air), building (e.g., Y/N);

■ global cell ID, PCI value, frequency, IsGPS synchronization, DL Tx configuration (i.e., number of Tx ports, maxTx power, DL bandwidth);

■ tower construction type (i.e., roof, monopole, building side, etc.), cable length and loss; antenna type (i.e., omnidirectional, directional), latitude, length, antenna height AGL (i.e., above ground), tower footing height MSL (i.e., average sea level), ground level;

■ antenna azimuth, elevation, mechanical down-tilt, electrical down-tilt, gain, H-beam width, V-beam width;

■ cell bandwidth, TA, neighbor cell list, PRS configuration;

■ eNodeB sector antenna array configuration.

Revisiting UE power consumption in IoT applications, the IoT-enabled LTE mode may seek further power reduction options. For example, one option would be for the UE to send ranging signal data only when the UE is already connected (to the network). Another option is for the UE to transmit ranging signal data only when some conditions are met that favor achieving high location accuracy, such as when the number of detected towers is greater than a threshold number N and/or the SNR/SNIR value exceeds a certain level. Note that: this method may not be applicable when an instantaneous position fix is required, but is acceptable for tracking (determining trajectory) the UE on the move when a certain amount of delay is acceptable.

In LSU embodiments, all LSU components/elements (such as a communications processor, a downlink signal processor, an uplink signal processor, and a navigation processor) may be implemented in software executable on one or more network core elements. In one embodiment, these LSU components may also run on an evolved 4.5G Mobile Edge Computing (MEC) server at the edge of the facility, whereby, for example, the LSU components may be integrated as hosted apps on a 4.5G MEC.

In another embodiment, LSU components in a 5G deployment may be hosted in a core network computing cloud. In this embodiment, the LSU hosted in the core network computing cloud supports "location as a service" (LaaS) data transport, whereby the UE acts as a gateway for the core network computing cloud and LaaS, exclusively for protected physical location data.

All LSU components/elements (communication processor, downlink signal processor, uplink signal processor, and navigation processor) are implemented in software.

The following is a further description of system deployment options, including the LSU arrangement:

LSUs may be deployed within a core network and/or an operator's IP services network.

LSUs may be deployed on servers at edge facilities of cloud computing-based centralized RAN (C-RAN) baseband processing, such as evolved 4.5G Mobile Edge Computing (MEC) servers, where LSUs may be integrated as hosted applications. Note that: the RAN is a radio access network.

3. The LSU is hosted in a core network computing cloud and/or an operator's services network cloud.

The LSU may be a fully hosted and managed cloud service connected to the operator's network core and/or core IP services network via a secure remote internet connection.

Embodiments 3 and 4 support location as a service (LaaS) data transport, whereby the UE acts as a gateway for the core network computing cloud and LaaS, dedicated to protected physical location data. However, three were chosen specifically for 5G networks with an Evolved Packet Core (EPC), also referred to as network core, hosted in the computing cloud. Meanwhile, selecting four is more suitable for wireless network selection of the current deployment and 5G deployment, and therefore is one embodiment. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the embodiments as claimed.

Fig. 39 illustrates an embodiment of a system employing a fourth option for LSU deployment based on an architecture with UE positioning within E-UTRAN (3GPP TS 36.305 release 14.3.0 release 14). This implementation may utilize the Open Mobile Alliance (OMA) Secure User Plane Location (SUPL) architecture version 2.1 (OMA-AD-SUPL-V2 _ 1-20120529-C). Unlike conventional E-UTRAN positioning with LTE control plane, SUPL performs E-UTRAN positioning over LTE user plane. The SUPL solution may utilize existing standards, such as the LTE interface and protocols to support UE positioning, to determine SUPL Enabled Terminal (SET) location over user plane bearers such as IP transport assistance data and positioning data. To accomplish this task, the SUPL solution is expanding on existing LTE interfaces and protocols, such as the LPP protocol. Note that: the SET is a logical entity in a device (i.e., UE) capable of communicating with a SUPL Location Platform (SLP). The SLP is responsible for location service management and location determination. SLP contains SLC and SPC functions. A SUPL Positioning Center (SPC) entity is responsible for all messages and procedures required for location calculation and assistance data delivery in a SUPL network. A SUPL Location Center (SLC) coordinates the operation of SUPL in a network and interacts with settings on user plane bearers.

Fig. 40 shows a block diagram of an embodiment of a location-based system architecture. The figure shows a) one ormore LTE devices 4001, also referred to as UEs; b)E-UTRAN eNodeB 4002; c) network core element: a Mobile Management Entity (MME)4005, an evolved serving mobile location center (E-SMLC)4009, a Serving Gateway (SGW) 4003, and a Packet Gateway (PGW) 4004; d) a core IP services network ofnetworks 4006; e)PoLTE LSUs 4007 and d) stand-alone LMUs or LMUs integrated withDAS system 4010. Included in fig. 40 is an IPservice network entity 4008 capable of managing/supporting location services (LCS) tasks. Note that:cell 4008 may also include other entities. Further, LTE and SUPL interfaces and user planes are identified in fig. 40.

This embodiment utilizes SUPL Location Platform (SLP) solution to determine device (UE) location. However, there are several important architectural differences between SUPL and PoLTE system architectures, which require the extension and/or modification of SUPL solutions and the introduction of new elements/functions. Listed below are these architectural differences:

first, the system shown in FIG. 40 does not include an SLP. Instead, the SLP function is performed by theLSU 4007.

Second, the SUPL architecture is to place a SUPL Location Platform (SLP) with a network core as a stand-alone entity or to combine some elements of the location platform with network core components, e.g. integrating a SUPL location center (SPC) in theE-SMLC 4009 or attaching an SPC thereto via a proprietary interface (see 3GPP TS 36.305 version 14.3.0, 14 th edition, section b.2, SUPL 2.0 and LTE architecture). However, to support LaaS, LSUs are hosted in a cloud outside the network core and/or core IP services network. Thus, in this architecture, the positioning element may not be integrated with the core network component and/or the serving network component. Instead, the information may first be carried to a relay (see fig. 40) entity that is integrated in the E-SMLC and acts as a relay to pass the information to the LSU using the SUPL lp interface (by having an extended interface protocol) or via the user plane via a proprietary interface and/or protocol.

Third, for downlink positioning, SUPL obtains assistance data from the SET, i.e., operations that occur entirely in the LPP/LPPe session. However, in some downlink positioning cases, information from the eNodeB and the UE needs to be combined for positioning operations. SUPL is, by definition, carried on the user plane and does not support operations terminated at the eNodeB. Therefore, operations requiring assistance data provided by various enodebs must be performed in conjunction with control plane procedures on LPPa (see 3GPP TS 36.305 release 14.3.0, 14 th edition, section b.4, procedures combining C-plane operations and U-plane operations). LTE release 14 solution is to integrate the SPC (SUPL location center) in the E-SMLC or attach the SPC thereto via a proprietary interface.

However, this solution is limited to assisting data collection/transmission, e.g., PoLTE uplink AoA/DoA and U-TDOA location operations that do not support transmission of digital samples requiring reference signals. In addition, this solution places some positioning functions in the core of the network and, according to the above-mentioned architectural difference discussions, it is not possible to support a PoLTE system architecture (fig. 40), where all of the positioning functions belong to a single entity (LSU) outside the network core and/or the core IP service network.

Fourth, the PoLTE operation requires collection/extraction at the UE and/or LMU and transmission of baseband I/Q samples of the reference signal in the time domain or digital samples of the baseband I/Q samples in the frequency domain to the LSU, or Resource Elements (REs) of the OFDM symbol carrying the reference signal or REs of the reference signal from the OFDM symbol. This is a unique requirement that is neither supported by the LTE positioning standard (i.e. the architecture used for UE positioning within E-UTRAN) nor by the OMA SUPL architecture.

This approach has a number of advantages, of which the main ones are listed below, while other advantages are noted throughout this disclosure:

the UE relieves the extensive computational burden required for positioning (RSTD computation), enabling significant UE power consumption improvements, which is very important for modems for internet of things (IoT) applications. At the same time, the method allows the positioning engine to run continuously in the background, enabling everywhere high accuracy positioning that provides up-to-date location information with low latency and without compromising UE power consumption.

2. Location Management Unit (LMU) complexity is significantly reduced, enabling seamless LMU integration with enodebs. In its current form, LMUs are complex, stand-alone devices that are completely dedicated to receiving and processing uplink reference signals to calculate relative time of arrival (RTOA) values (also referred to as measurements). Thus, in its current form, LMUs are not easily incorporated into enodebs. On the other hand, collecting/extracting digital samples of the baseband reference signal and transmitting them to the LSU is a low complexity task (effort) presenting only a small computational load.

The LSU may be placed within a network and/or an IP services network, or outside of a network hosted in a computing cloud. Thus, LaaS is enabled for most current and future network architectures/environments.

4. Digital samples of baseband I/Q samples of the reference signal can be carried through the control plane and/or the user plane, enabling uplink positioning on the user plane, which cannot be achieved by SUPL solutions.

5. The ability to process both uplink and downlink reference signals in a single entity LSU improves positioning system reliability and position fix accuracy by making possible progressive features that are not feasible before, for example, joint uplink/downlink or downlink/uplink UE positioning.

6. Capability to handle multiple networks, combine bluetooth, WLAN, LTE, etc.

Referring to fig. 40, the SET and LMU functions are modified and the underlying protocol (not shown in fig. 40) for delivering messages/information over the LTE/SUPL interface (fig. 40) is extended to accommodate system requirements. In addition, a new relay entity is introduced (see fig. 40). The relay entity is integrated in the E-SMLC that passes digital samples of the uplink baseband reference signal, assistance data and other information to the LSU.

As previously described, the LMU functionality may be implemented as a stand-alone LMU unit or integrated with other elements of the wireless network infrastructure, such as LMUs integrated with enodebs, LMUs integrated with DAS systems, and the like. However, in all variants, digital samples of the relevant UE uplink baseband reference signals are collected and pre-processed and will be forwarded, together with assistance data and other information, to the LSU for further processing.

In addition, SLP and its major components SLC and SPC are also modified and incorporated into the LSU. For example, the modifications include uplink and/or downlink positioning using signal processing and positioning calculation algorithms/techniques, adding support for receiving/retrieving uplink/downlink digital samples of baseband reference signals, extending assistance delivery functionality to accommodate uplink positioning, and the like. Furthermore, the Lup and lp message support and underlying protocols are extended to enable the transmission/transfer of digital samples of the baseband reference signal from the relay entity to the LSU.

The set of modified examples includes the ability to retrieve, pre-process and transmit digital samples of downlink baseband reference signals to the LSU by a SUPL location computation function (SPCF). Additionally, SPCF modification may include preprocessing of these samples prior to transmission to the LSU. SUPL has extended the LTE Positioning Protocol (LPP) for delivering information, including assistance information, between a UE and a network core element over the Uu interface and over an eNodeB, and also extends SLP over Lup. However, in one embodiment, the extended LPP protocol, LPPe, is further extended to accommodate digital sample transmission. On the other hand, the SET function can be simplified by limiting it to retrieving and sending digital samples to the LSU and pre-processing these samples.

Essentially, for any of the LMU functions described above, an alternative LMU function implementation is employed-simply the ability to retrieve, pre-process and send digital samples of uplink baseband reference signals to the PoLTE LSU via the relay entity. Furthermore, the eNodeB functionality may be extended to retrieve, pre-process and send digital samples of uplink baseband reference signals directly to the PoLTE LSU or via a relay entity.

In addition, the LMUs need not obtain assistance information from the eNodeB and the E-SMLC, as this information may be collected by the relay entity, e.g., the E-SMLC delivers the assistance information directly to the relay entity. The LMU communicates with the E-SMLC and the relay entity through the SLm interface using the underlying SLm-AP protocol. The latter is extended to accommodate digital sample transmission and LMU/relay communication.

This embodiment introduces a new entity, the relay entity. The purpose of this embodiment is to address the current LTE/SUPL architecture (3GPP TS 36.305 release 14.3.0 release 14, section b.4, procedures operating in conjunction with the C-plane and U-plane) limitations mentioned in the architecture difference discussion (third point). The relay entity transmits the digital samples from the LMUs to the server. In addition, the relay entity enables communication between the server and the E-SMLC to allow assistance data transfer to the server via LTE positioning protocol annex (LPPa) delivery. LPPa allows the eNodeB to exchange location information with the E-SMLC for UE positioning purposes, including assistance data. The assistance data may already be available at the E-SMLC at the time of the server request, or the E-SMLC retrieves the assistance data from the appropriate eNodeB. Note that: the assistance data may be used for uplink AoA/DoA, U-TDOA, downlink OTDOA, and E-CID positioning. The relay entity communicates with the server over an lp interface or a proprietary interface. In the case of the lp interface, its message support and underlying protocols are extended to enable transmission/transfer of digital samples of baseband reference signals and/or assistance data.

The specific implementation of the IPservice network entity 4008 is MNO related. An MNO is a mobile network operator, also known as a wireless service provider, wireless operator, cellular company, etc. Thus, the server andunit 4008 command/control communication interface may be MNO specific. In this embodiment, it is assumed that all communications with the server are over the internet and that the connection is secure. In addition,unit 4008 will transmit positioning information exchanges between the server and the system elements. There are several ways to guide this information exchange, for example using protocol tunneling techniques.

This embodiment utilizes the user plane to locate data transmission and communication within the system. However, the introduction of a relay entity enables another system embodiment that utilizes a control plane to locate data transmissions and communications within the system. As previously mentioned, conventional E-UTRAN positioning employs the LTE control plane for positioning data transmission and communication. Observable objects, i.e., RSTD, RTOA, AoA, etc., as well as data and assistance information are carried between the UE and/or LMU and the E-SMLC via the underlying LPP, LPPa and Slm-AP protocols. The E-SMLC is responsible for location fix determination (fig. 39). Similarly, for embodiments, the LPP/LPPa and Slm-AP protocols are extended to support transmission/delivery of digital samples of baseband reference signals and/or PoLTE-specific assistance data to the E-SMLC and ultimately to the relay element. The relay communication/data exchange with the server is via an IP service network entity 4008 (fig. 40). The interface and protocol may be proprietary or may be one of the LTE/SUPL interfaces with a modified/extended underlying protocol.

Furthermore, this alternative system embodiment results in a reduction in complexity: for example, the SET function is simplified or the entire SET element is eliminated; in addition, the SLC and SPC functions of the SLP incorporated in the server (in one embodiment) are significantly simplified or even completely removed.

Another embodiment is shown in fig. 41. This embodiment may utilize only a small subset of the functions defined by the SUPL architecture and include stand-alone LMU functions and their associated E-SMLC, or indeed only LMU functions in the eNB. Listed below are specific modes of operation for this embodiment:

the UE client and LSU exchange data packets in a proprietary format over IP protocol, and:

a. the data packet content may include measurement data, assistance information, or control commands.

b. Data packets may be transmitted over the LTE user plane or any alternative form of secure data bearer such as Wi-Fi (4011) and/or Wi-Fi plus ePDG (4012) combination using radio level integration (LWIP) technology such as 3GPP LTE-WLAN aggregation (LWA) or 3GPP LTE with WLAN over IPsec Tunnel, for example. (FIG. 41).

i. Note that: theePDG entity 4012 is responsible for interworking between the evolved packet core (LTE EPC) and non-3 GPP networks that require secure access, such as Wi-Fi, LTE metropolitan area networks and femtocell access networks.

c. Data packets may be exchanged directly between the UE and LSU, or via an internet of things (IoT) platform such as Amazon Web Services (AWS) IoT, google cloud, or AT & T M2 x.

i. Optionally, an internet of things (IoT) platform may be part of the IPservice network entity 4008.

E-SMLC and LSU exchange data packets in a proprietary format over IP protocol and:

a. the data packet content may include measurement data, assistance information, or control commands.

b. Data packets may be exchanged directly between E-SMLC and LSU, or via an internet of things (IoT) platform such as Amazon Web Services (AWS) IoT, google cloud, or AT & T M2x (fig. 41).

LMUs (and/or LMUs in enbs) and LSUs exchange data packets in a proprietary format over IP protocol, and:

a. data packets may be exchanged directly between LMUs and LSUs, or via an internet of things (IoT) platform such as Amazon Web Services (AWS) IoT, google cloud, or AT & T M2 x.

b. Data packets may also be exchanged between LMUs and LSUs through E-SMLC (fig. 41).

In one embodiment, a wireless device of an exemplary LTE network may interact with one or more communication networks and/or one or more dedicated positioning systems (networks). These networks (systems) may employ downlink or uplink positioning or both.

Downlink positioning involves a wireless device receiving signals from one or more networks/systems for device positioning. Currently, downlink positioning requires wireless devices to detect and process these signals, whereby the processing involves computing one or more of the following observable objects: GPS/GNSS-pseudorange, pseudo-doppler, etc., timing TOA, TDOA, etc., direction AOA, arrival phase, etc., utilized in wireless device location calculations (computations) by the network component (E-SMLC). Alternatively, the wireless device may determine its own location by performing both of the above calculations.

Uplink positioning refers to one or more specific network elements receiving signals for device positioning from one or more wireless devices. Similarly, similar to the downlink, current uplink geolocation also requires that the network element (e.g., LMU) detect and process these signals and perform calculations while the position calculation is performed by another network component (E-SMLC). Additionally, in some cases, the network element may also calculate the location of the wireless device.

The wireless device and/or network element may also receive assistance/assistance information messages supporting positioning. These messages and signals are combined, although in some implementations they may be used for device location. For example, GNSS messages contain ranging codes (for timing calculations) and navigation data (assistance information).

In some cases, signals used for wireless device positioning are particularly used for device positioning purposes, i.e. dedicated use. However, in other cases, these signals have dual purposes. For example, pilot signals present in many communication network transmissions may also be used for device location.

In this embodiment (unlike the current implementation), neither the wireless device nor the particular network element calculates an observable of the signal. Instead, the wireless device and/or particular network element collects and pre-processes one or more snapshots of the signal for wireless device location, where the snapshots are to be transmitted to the LSU, which determines the observable of the signal, including GPS/GNSS pseudoranges/pseudodoppler, etc., and performs wireless device location. The computational load of collecting and pre-processing one or more snapshots is at least one order of magnitude lower than the currently required computational power and resources. Thus, in this embodiment, the wireless device and/or the particular network element are relieved from the heavy computational burden and require a smaller amount of computational resources.

When communicating with a Location Server (LSU), snapshot data consumes more bandwidth than current implementations. Some of the energy savings realized in this embodiment will then be offset by the extra power consumed in transmitting the snapshot data. However, the present disclosure still yields substantial power savings from the inventors' power estimation calculations.

At the same time, the advantage of this embodiment is superior to the larger communication overhead of the snapshot. For example, the overhead of the snapshot's larger bandwidth is very small, e.g., no more than 1% (in any category) of the LTE uplink bandwidth. In addition, the larger snapshot communication overhead is not relevant (impact) with respect to the communication of a particular network element with the LSU.

Listed below are the advantages of embodiments of the present invention:

wireless devices-longer battery life, better energy efficiency, complexity, lower cost, and smaller size.

Specific network element —

1) For the individual elements to be fully dedicated to receiving, detecting and processing signals for the device to locate and compute observable objects, there is a significant reduction in complexity and lower power consumption, which in turn enables seamless integration with other network elements.

2) For other network elements, it is easy to extend the functionality of the network element, including collecting and pre-processing signal snapshots for wireless device location, and sending the snapshots to the LSU.

For example, the location management/measurement unit (LMU) of a cellular network is a complex independent component that computes observable objects of signals, which current form cannot be easily merged with another cellular network component, eNodeB, without affecting the hardware and software of the eNodeB. On the other hand, collecting and pre-processing snapshots and sending them to the LSU is a less complex task, providing only a small additional computational load to the eNodeB.

Another example is an AP, WLAN component, which may be responsible for processing one or more signals for wireless device localization, including computing observable objects of these signals, resulting in a significant computational and computational resource burden. Furthermore, the computational constraints of already installed APs (legacy) will prevent the deployment of prior art positioning algorithms with high computational load. At the same time, the present embodiment does not occupy the computational resources of the AP, since snapshots are collected and preprocessed and sent to the LSU, thus presenting only a small computational load of the WLAN AP.

Other beneficial effects include-

1) Deployment of high computational bandwidth advanced positioning algorithms provides excellent positioning accuracy, reliability without occupying existing network infrastructure component resources.

2) Greatly reducing (minimizing) upgrade logic effort; without the HW/SW legacy limitations.

3) Prior art machine learning is deployed to further improve positioning reliability and position and orientation accuracy.

4) In the LSU, i.e., in the background, wireless device location is continuously ongoing without increasing power consumption and without tying up wireless device/specific network component computing resources.

In embodiments of the present invention, signal snapshots for device localization may be collected in a digital format (i.e., digital samples). The digital samples are from a baseband signal; the digital samples may be in the time or frequency domain and may be represented by I/Q samples, Resource Elements (REs), Channel State Information (CSI), and so on. The digital samples are also per antenna. Note that: the RE and CSI are complex-valued coefficients per OFDM subcarrier. The I/Q values represent the in-phase (I) and quadrature (Q) components of the signal.

In one embodiment, elements of the wireless device and/or elements of the network may include a detector (also referred to as a logic entity) configured to detect and extract digital samples of signals used for device location, and may also be configured to collect and store a plurality of digital samples for each antenna and each signal Identification (ID). Further, the detector may be configured to collect/store metadata, such as each frame start, as well as other assistance/assistance information.

In one embodiment, the detector may also be configured to pre-process the collected (stored) digital samples, for example to reduce the data size, e.g., reduce the communication bandwidth, before sending to the LSU. This reduction may include extracting digital samples representing only the signal used for device positioning, i.e., excluding digital samples carrying the payload.

In one embodiment, the detector may be configured to collect and retain a subset of digital samples of data in the GPS/GNSS navigation message frame, i.e. a small portion of the message. This approach is feasible because most of the information carried in the navigation message will be available at the LSU (GPS/GNSS assistance data), including reference time, reference position, satellite ephemeris, clock corrections, ionosphere models, earth orientation parameters, GNSS time offsets, acquisition assistance, Almanac, UTC models, etc.

In one embodiment, the detector may be configured to obtain further data size reduction by compressing the digital samples into smaller numbers of bits. Compressing the digital samples may be accomplished by executing a correlation engine in the detector via the following method.

And (3) emitter detection: the reference signal has high auto-correlation and low cross-correlation properties to support transmitter (source) detection (discrimination) during simultaneous transmissions from multiple transmitters. Note that: the reference signals may be distinguished based on the ID/parameters of the transmitter and/or the reference signal configuration. The OFDM symbols containing the reference signal are captured by the receiver. Any number of OFDM symbols containing reference signals limited only by available memory may be captured and processed. Thus, the input to the emitter detection engine is a complex two-dimensional array. The input data elements comprise a superposition of reference signals from a plurality of transmitters. The transmitter detection engine searches the input data for the presence of a known reference signal generated by each transmitter in a given location database/list. The search is performed by cross-correlating the input data reference signal with a known reference signal (i.e., an ideal copy of the reference signal at the transmitter antenna, including modulation). Thus, the transmitter detection function includes ideal copy generation, resulting in an ideal copy of the reference signal associated with the ID/parameters and/or reference signal configuration of the transmitter. Similar to the input data, the ideal replica is in the form of a two-dimensional array, containing the same number of REs as the positioning occasion data. The replica is also indexed by the same frequency sample index and symbol index.

Cross-correlation: the transmitter detection engine performs cross-correlation in the frequency domain by multiplying each received OFDM symbol with the conjugate of an ideal OFDM reference signal. This is the frequency domain equivalent of the time domain matched filter. When the cross-correlation between the received reference symbols and the ideal reference symbols has been applied, any coding scheme used to create the reference signals has been removed and the residual phase information for each subcarrier is preserved. The result of this operation is a phase coherent two-dimensional array. The phase coherent two-dimensional array is then integrated by summation. Any number of OFDM symbols may be integrated using this method. The magnitude and phase of the complex values in the output vector correspond to the TOA and the presence of a reference signal sent by the transmitter at the receiver.

And (3) detection: the detection process examines the magnitude and phase of the cross-correlated output to determine whether the reference signal sent by the transmitter and captured at the receiver has sufficient characteristics to correctly estimate the TOA of the received signal.

Compression: if the properties of the signal are sufficient, the output of the cross-correlation is marked as valid and the non-zero part of the complex vector is sent to a quadratic processing block for TOA estimation. In this approach, the TOA estimation block may be located remotely from the transmitter detection engine.

In the case of using downlink positions of CRS data received from a 2x MIMO transmitter, there are 40 CRS OFDM symbols in one frame of LTE data. In the case of a 10MHz LTE bandwidth, 600 subcarriers are contained within each OFDM symbol. If the receiver utilizes a 16-bit modulo receiver, each composite subcarrier has a total bit depth of 32 bits. For data consisting of CRS data of one frame value, the total bit size of the data set (unit is 40 × 600 × 32 bits) or 768000 bits.

However, using coherent compression schemes, the size of the resulting data used for TOA estimation is reduced to the non-zero portion of the cross-correlation output multiplied by the number of detected transmitters. The size of the data detected by each transmitter is reduced to 1 x 200 x 32 bits, or 6400 bits.

In the case where the uplink position uses SRS data received at the base station with the subframe configuration of 10, there are 8 SRS OFDM symbols in the LTE data of one frame. In the case of a 10MHz LTE bandwidth, 600 subcarriers are contained within each OFDM symbol. If the receiver utilizes a 16-bit modulo receiver, each composite subcarrier has a total bit depth of 32 bits. For data consisting of SRS data of one frame value, the total bit size of the data set (unit is 8 × 600 × 32 bits) or 1536000 bits.

Assuming a full bandwidth SRS signal, the size of the data detected by each transmitter is reduced to 1 × 288 × 32 bits, or 9216 bits in this case.

The coherent compression process reduces the amount of data required for the number of symbols used in the cross-correlation process.

In addition, there are many compression algorithms that can further reduce the data size. Some compression algorithms are well known, such as a-law and U-law hybrid algorithms. Some less known algorithms are also used in radar technology, while others are directed to fiber optic data transmission. Thus, the detector may be configured to compress the digital sample size, for example, from 32 bits to 16 bits or from 32 bits to 8 bits, resulting in a 2-fold or 4-fold reduction in data size.

In one embodiment, the detector may be configured to perform additional pre-processing (to further reduce data size), including matched filtering of the signal for device localization, computing Singular Value Decomposition (SVD) principle eigenvalues of a matrix formed by one or more of the digital samples of the signal. This additional pre-processing is a trade-off between computational load/resources and reduced communication bandwidth with the positioning server.

The data size of the digital samples may be further reduced, for example, by matched filtering in conjunction with Carrier Frequency Offset (CFO) processing based on the network node ID, and then integrating (over time) the multiple samples. In one embodiment, the data associated with the detection of the signal used to determine the position is greater than the backhaul data used to transmit the compressed signal. This is especially true in IoT deployments. For example, a Cat-M modem that typically uses a 1.4MHz bandwidth on the downlink to communicate with an LTE network may see LTE signal bandwidth up to 10MHz, including CRS signals that may be used to determine location. This is possible because the Cat-M mode is typically deployed within the 10MHz channel (in-band) used for normal LTE communications. The compressed information of the downlink signal may then be sent back to the LSU using a Cat-M uplink channel or using Wi-Fi, bluetooth, ZigBee, other IEEE 802.15 wireless technologies, or other low bandwidth communication technologies now existing or developed in the future. The same type of implementation may be used for NB-IOT, whereby the modem may take more signals (e.g., Cat-M downlink signals) and compress them before sending them back to the LSU over the NB-IOT data channel. An equivalent method may be used in the reverse direction of the eNB, whereby the uplink signal may be compressed and then passed to the LSU using a lower bandwidth communication protocol, such as those described above. This enables the position solution to make more signals available for position calculation purposes. This may also lead to better location results because: 1) it is well known that position accuracy is inversely proportional to bandwidth, and having more bandwidth results in better position accuracy; 2) having more bandwidth reduces the chance of signal rejection by interferers and also allows better multipath rejection, especially in view of the super resolution techniques described herein; and 3) allowing more signals to be available for integration.

In one embodiment, an exemplary positioning LTE network, assistance/assistance information includes:

a. serving cell information: [ physical cell Id, Global cell IdEUTRA-and UTRA, earfcn-DL, System frame number, timeslot number, UTC timestamp, rsrp-result, rsrq-result ];

b. ue-RxTxTimeDiff, uplink path loss, bandwidth in a Physical Resource Block (PRB).

c. Neighbor cell information (0 to 32 neighbor cells): [ physical cell Id, Global cell IdEUTRA-and UTRA, earfcn-DL, System frame number, rsrp-result, rsrq-result ];

and d, UE information: [ UE ID, UE category, mobility state, mobility history report ].

In one embodiment, an exemplary positioning LTE network, assistance/assistance information includes:

a. site name:

b. technology (i.e., 4G, 5G), active (i.e., in air), indoor and outdoor;

c. global cell ID, PCI value, frequency, IsGPS synchronization, DL Tx configuration (i.e., number of Tx ports, maxTx power, DL bandwidth);

d. tower structure type (i.e., roof, monopole, building side, etc.), cable length and loss; antenna type (i.e., omnidirectional, directional), latitude, length, antenna height AGL (i.e., above ground), tower footing height MSL (i.e., average sea level), ground level;

e. For each antenna used for signal acquisition: antenna azimuth, elevation, mechanical down-tilt/electrical down-tilt, gain, H-beam width, V-beam width;

f. optional cell bandwidth, TA, neighbor cell list, PRS configuration;

eNodeB sector antenna array configuration.

In one embodiment, sounding reference symbols transmitted by a UE may be acquired and reported along with assistance information related to configurations assigned to the UE at the time of SRS transmission. This information may include some or all of the following elements of the UL configuration IE according to 3GPP TS 36.355:

-cell PCI

-call timing advance

Cell UL bandwidth

-Ul-cyclic prefix length

Cell srs bandwidth configuration

-UE srs-bandwidth

-UE srs-antenna port

-Srs-frequency hopping width

-Srs-cyclic shift

-Srs-configuration index

-a transmission comb

-frequency domain position

-implementing packet frequency hopping

-δSS

SFN initialization time

In one embodiment, sounding reference symbol data acquisition may be supplemented or replaced by acquisition of uplink demodulation reference symbols transmitted by the UE.

In one embodiment, the demodulation reference symbols should be supplemented by the following assistance/assistance information:

push transmission start: frame and subframe number

b. Uplink grant information:

(i) Frequency hopping marker

(ii) Resource block allocation and frequency hopping resource allocation

cyclic shift of DM RS and OCC indices

And d, PUSCH configuration:

e.n-SB

f. frequency hopping pattern

Pusch frequency hopping offset

Ul-reference signal PUSH:

(i) the frequency hopping of the packet is realized,

(ii) packet allocation PUSCH

(iii) Implementing sequence frequency hopping

(iv) Cyclic shift

In one embodiment, more than one demodulation reference symbol block and associated side information may be acquired and reported.

In one embodiment, exemplary snapshot metadata information includes: communication network/positioning system description and type, location, signal classification (downlink or uplink), signal description, type, structure and parameters, date/time stamp, snapshot data size, source of creation data (wireless device/device ID, network/system element (network/system element ID, etc.), data compression information, assistance/assistance information availability, time offset of first reported digital samples relative to the start of a radio subframe, etc.

In this embodiment, the data of the snapshot may be grouped (by logical entities) with its metadata and associated assistance/assistance information and referred to as a positioning opportunity. The positioning occasions may occur periodically.

According to one embodiment, the logical entities of the wireless device and/or the logical entities of the network elements may include a communication processor configured to organize and exchange positioning occasion data (snapshot data, metadata, assistance/assistance information) with the LSU; and to receive commands and other information. It should be noted that the signaling may conform to industry standard interfaces/protocols, such as the OMA SUPL protocol and/or 3GPP LPP, internet protocol security (IPsec), and the like. Additionally, proprietary interfaces and/or protocols may be utilized.

In this embodiment, the LSU may include a positioning engine configured to determine the wireless device position/location from one or more related positioning occasions. The output of the engine may also include location related metadata (metrics) such as confidence radius values, GDOP, fourth time report from FCC, and commanded wireless E911 location accuracy metrics.

In one embodiment, the LSU may include a location management processor configured to interact with one or more wireless devices, one or more communication networks/location systems-components and/or services of the networks/systems, and a location engine. The location management processor is configured to: a) managing one or more wireless devices, networks/systems (elements of networks/systems, network services, and LSUs); b) manage delivery of location opportunities (metadata and/or assistance/assistance data of snapshots) from one or more logical entities to a location engine, and c) security.

In one embodiment, a location management processor receives positioning opportunities (from one or more logical entities) and passes this information to a positioning engine for target location calculation and tracking, i.e., navigation. The positioning engine then returns (to the location management processor) the location of the target and tracks the information along with location/tracking related metadata, which may be provided, for example, to services of the network, wireless devices, etc.

Pinpointing methods that employ a two-step localization approach, where the first step requires the calculation of one or more observable objects (observations): TOA, TDOA, TOF, AOA/DOA, received signal phase, and observable objects associated with these outcome measures (SNR, standard deviation, confidence, etc.). During the second step, the observations and their metrics are used to determine the wireless device (target) location/navigation.

Meanwhile, there are other positioning methods using a one-step positioning process, such as probabilistic positioning, including RF fingerprinting, direct position determination, etc., i.e., without the aforementioned observation (observable). However, the somewhat lower complexity of one-step positioning comes at the cost of reduced accuracy, positioning ambiguity, and other performance limiting phenomena.

According to the present disclosure, the observable object (TOA, TDOA, TOF, etc.) accuracy of the two-step process can be uniquely enhanced by multi-path suppression using the advanced spectrum estimation (super-resolution) algorithm described above. Similarly, AOA/DOA unique enhancement/adjustment combines the aforementioned super-resolution estimation of the time difference of the ranging signals (i.e., TDOAs) received at each antenna with an AOA/DOA technique that compares the phase difference of the ranging signals collected by each antenna.

In accordance with the present disclosure, the accuracy and robustness enhancement of the second step in the above-described two-step positioning process (positioning/tracking) includes heuristic point-to-point positioning/tracking techniques and adaptation of general positioning algorithms into communication network signal structures and network environments.

According to the present disclosure, the two-step approach also accommodates a one-step positioning process. The logical entity of the wireless device and/or the logical entity (detector) of the network element is configured to detect and extract probability statistics (data) for device positioning and may also be configured to collect and store this information from the antennas and from the Identification (ID) of each signal. Further, the detector may be configured to collect/store metadata and/or other assistance/assistance information. The detector can group these probability statistics with its metadata and associated assistance/assistance information to generate probabilistic positioning opportunities. A communication processor included in the logical entity is then configured to organize and exchange these positioning occasion data with the LSU.

In one embodiment, the positioning engine comprises a signal processing unit and a data processing unit according to a two-step approach; wherein the signal processing unit receives positioning occasion information from the position management processor and the position management processor receives the position of the object and tracking information together with position/tracking related metadata/metrics from the data processing unit. Each unit utilizes the assistance/assistance information included in the positioning occasion and the metadata of the snapshot.

In one embodiment, the signal processing unit may be configured to estimate observable objects and their metrics for downlink signals and/or uplink signals from one or more communication networks and/or one or more dedicated positioning systems (networks).

In one embodiment, the signal processing unit may be configured to estimate Carrier Frequency Offset (CFO) using one or more specific algorithms and/or techniques, which may allow the signal processing unit of the LSU to track one or more mobile wireless devices and suppress clock frequency mismatches between one or more network nodes and the wireless device. The CFO estimation is used to correct the snapshot digital samples of the computational observable.

In one embodiment, the data processing unit may be configured to use the output (i.e., observable/metric) of the signal processing unit to perform positioning and tracking. In addition to the position and orientation of the target, the data processing unit also generates measurement data comprising a covariance matrix of a plurality of positioning results obtained from a combination of a plurality of observable objects and their standard deviation values.

In one embodiment, the data processing unit may be configured to perform downlink and uplink positioning/tracking using observable objects and their measurements obtained from one or more communication networks and/or one or more dedicated positioning systems (networks). The data processing unit may also be configured to use a combination of downlink and/or uplink observable/measurements from one or more communication networks and/or one or more dedicated positioning systems (i.e., hybrid positioning/tracking).

In one embodiment, the data processing unit may utilize multipoint positioning (also known as hyperbolic positioning), three-point positioning, triangulation, and excellent DOA/AOA/E-CID positioning methods to obtain the position fix and its measurements. Multi-point localization is further enhanced by forming a location cost function with confidence measures to increase the probability of identifying a global minimum (e.g., location fix) of the cost function.

In one embodiment, the signal and data processing unit may be configured to execute a probabilistic positioning algorithm to determine the wireless device location fix, i.e., to perform a one-step positioning process utilizing probabilistic positioning occasion information from one or more wireless devices and/or logical entities of one or more network elements.

Wireless network deployments sometimes refuse to use the multipoint positioning/three point positioning method, which requires at least three reference points (for 2-D positioning) in order to obtain the wireless device (UE) location. For example, in some environments, only two high power cells or APs are deployed, thereby flooding a large area of RF signals. This creates problems with these approaches that rely on at least three detectable reference points. To alleviate this problem, the present disclosure employs heuristic point-to-point location/tracking techniques and adapts the general location algorithm to the signals within the communication network signal transmission structure and network environment.

In one embodiment, the multi-point/three-point method is further enhanced by dividing the set of all detectable reference points (nodes of the network) into three or more subsets, determining a target location for each subset, and performing target location by applying a location consistency algorithm (including a machine learning algorithm) to the resulting plurality of location estimates.

In cellular networks, the excellent DOA/AOA positioning of the present disclosure is based on the E-CID method described above. This is a single tower based uplink location procedure that utilizes two observable objects (round trip path delay (RTT) calculated by the serving cell and serving sector horizontal (azimuth) beamwidth). This approach lacks accuracy because the sector antennas in current deployments have large (60 or 120 degrees) horizontal planar beamwidths. As described above, the unique enhancements/adaptations of the present disclosure take advantage of existing sector antenna diversity (AOA/DOA estimation using MIMO sector antennas). The unique enhancement/adaptation described above effectively reduces the AOA angle error to less than one degree. However, in order to obtain a location fix in 2-D, it is necessary to know the distance from the UE to the serving cell in addition to the AOA/DOA estimate. The distance may be derived from a Round Trip Time (RTT)/Time Advance (TA) estimate, which is based on measurements performed by the serving cell. However, the serving cell receiver does not mitigate radio wave propagation phenomena, such as multipath, when estimating RTT/TA.

Therefore, RTT measurements may lack the required accuracy. One way to solve the accuracy problem is to calculate the above-mentioned distance (instead of RTT) from the downlink TOA observable between the serving cell and the wireless device (UE) using the algorithm of the present disclosure. A drawback of this solution is that the downlink TOA is measured unidirectionally and requires accurate synchronization between the serving cell and the wireless device, i.e. the positioning accuracy is affected by the synchronization error. On the other hand, because the wireless device is locked to the serving cell and mitigates the clock frequency mismatch provided by the CFO estimation and correction disclosed above, the error is reduced to not reduce the accuracy gain achieved by the algorithms of the present disclosure.

In cellular networks, Timing Advance (TA) is used to compensate for propagation delay as a signal travels between a UE (wireless device) and a serving cell sector antenna. The serving cell base station allocates a TA to the UE based on its measured distance of the UE (see fig. 37).

In LTEtiming advance type 1, the measurement (see fig. 38) corresponds to Round Trip Time (RTT), i.e., signal round trip propagation delay. However, the timing advance propagation delay may come from DLOS/direct path or reflected path and include propagation delay through cell tower cables and base station/UE electronics. Further, the UE adjusts its transmission timing with an accuracy of 4xTs, where Ts is the LTE system timing, equal to 32.55 ns.

Type 1 is defined as the sum of the receive-transmit time difference at the eNB and the receive-transmit time difference at the UE:

TA TADV (eNB Rx-Tx time difference) + (UE Rx-Tx time difference).

Therefore, the distance d to the base station is estimated using:

d ═ c (TADV/2), where c is the speed of light; or

d ═ c (RTT/2), where c is the speed of light.

Ta (rtt) may be obtained from the serving cell and represents an independent UE range estimate from the serving sector. However, the TA cannot be obtained from the UE (wireless device). Instead, the UE provides access to the receive transmission time difference (i.e., the UE's Rx-Tx). As described above, the UE's (Rx-Tx) RTT-eNB Rx-Tx. However, according to fig. 37 and 38, when adjusting the UE TA, the Rx-Tx time difference of the serving cell eNB will be the same for all UEs. Thus, the Rx-Tx measurements of the UE will still correspond to RTT.

The RTT is equivalent to the TOF/TOA estimate, which is biased according to the cell tower antenna cable length and base station electronics.

In one embodiment, the cable length propagation delay of the antenna may be estimated from the tower height, and the propagation delay of the base station electronics may be estimated from the statistics collected from the individual towers.

There are a number of situations where downlink and uplink joint positioning can be used to overcome the disadvantages of single downlink or uplink positioning. For example, in an environment where only signals from two nodes are available, the combination of downlink TOA/TDOA and uplink AOA/DOA (from the serving BS) estimates may improve location accuracy/reliability in the cellular network. This is because the additional uplink AOA/DOA constraint reduces the UE (wireless device) 2-D positioning ambiguity for the downlink, which is caused by the number of reference points (cells) being less than three.

In another example, additional constraints of AOA/DOA estimation may further improve the heuristic point-to-point location/tracking technique described above.

Furthermore, even if observable objects from three or more nodes are available, the additional AOA/DOA constraints help mitigate a number of network-inherent errors affecting the multipoint/three point positioning method, including node synchronization errors, poor GDOP, sector antenna coordinate errors (including altitude), uncompensated cable delay errors (calibration errors), and the like. The serving cell AOA/DOA estimate is free of these errors because the MIMO sector antennas used for AOA/DOA estimation are time and phase coherent. Note that: AOA/DOA LOB is equal to the angle between the baseline of the sensor (antenna) and the incident RF energy from the wireless device. If the angle between the baseline and, for example, true north is known, a true line of bearing (LOB) and/or AOA may be determined. The baseline angle error may also affect AOA/DOA accuracy, but the effect is lower than the overall effect from the error described above.

At the same time, RTT observable objects collected from the serving cell or wireless device may also provide additional TOF/TOA constraints, helping to mitigate the inherent errors of many of the aforementioned networks. Thus, downlink and uplink hybrid positioning may also include additional constraints for positioning error reduction (TOF/TOA observable from AOA/DOA and RTT).

Hybrid positioning may also span two or more networks. For example, indoor wireless device location may be implemented based on observable objects from WLANs and cellular networks. Systems based on WLAN positioning RSSI are very common, but not accurate; and because the clocks of the Access Points (APs) are only loosely synchronized with each other, the multipoint positioning method accuracy is severely affected. The lack of precise synchronization between the AP and the device also reduces TOA/TOF accuracy. For precise positioning, the AOA/DOA method is used with observable objects determined by WLAN APs equipped with MIMO antennas (typically with three or more separate antennas).

The unique enhancements of the unified framework/platform of the present disclosure, along with the AOA/DOA algorithm, enable the signal processing and data processing units of the LSU to compute an accurate LOB from one or more APs. Thus, such a hybrid approach to positioning based on a combination of WLAN and cellular networks would have a higher accuracy than a single network-based positioning, since joint positioning yields better spatial diversity against RF propagation phenomena and also mitigates the individual network drawbacks inherent.

In this embodiment, the data processing unit may utilize multipoint positioning (also known as hyperbolic positioning) for wireless device positioning. Hyperbolic navigation is based on timing differences, i.e., TDOA observable objects, without reference to a common clock. The data processing unit may be further configured to utilize the above-described measures of observable objects to mitigate ambiguity in multi-point position fixes, and to apply a position consistency algorithm, including a machine learning algorithm.

The multipoint location method requires solving multiple hyperbolic (TDOA is also known as RSTD/RTOA in cellular networks) equations for which multiple different algorithms/methods can be used to find the correct solution. Note that: the relative time of arrival (RTOA) observable is in the form of a TDOA in an uplink location in a cellular network.

In the overdetermined case where the equality is more than the unknown, i.e., four or more independent observable objects are available for 2-D localization, the solution involves an iterative approach starting from an initial estimate or "guess" of the wireless device location. Each iteration may then be utilized by determining a local linear least squares position solution to improve the estimate.

One drawback of this approach is that the initial position estimate is required to be close to the final position solution in order to guarantee convergence and/or the absence of local minima, which can lead to significant position errors. Overdetermined cases, on the other hand, reduce the likelihood of ambiguous and/or irrelevant solutions that may occur when only the minimum required number of observable objects (e.g., three observable objects for 2-D localization) are available.

According to the above, the iterative approach needs to have a close initial estimate of the UE position. This estimation can be enhanced by employing the hybrid approach disclosed above or one of the downlink and uplink joint positioning based on a single network.

The unified framework/platform capability of the present disclosure to handle enhancements/unique adjustments from various networks/systems in a single physical LSU, including all downlink/uplink signals, as well as positioning techniques, methods/technologies, enables advanced hybrid/converged positioning not previously feasible, including joint uplink-downlink or downlink-uplink wireless positioning that improves positioning system reliability and position location accuracy.

Returning to the discussion of the LSU embodiments, the location management processor of the LSU may also include a communication processor configured for signaling and information exchange with wireless devices of a communication network/positioning system (network), including elements of the network/system. The signaling may conform to the OMA SUPL protocol and/or 3GPP LPP/LPPa, or a combination of LPP, LPPa and SUPL, Internet protocol Security (IPsec), as well as other protocols used or available for communication with the network, such as the cellular LCS-AP protocol and/or the IPSec/IKEv2 protocol or the proxy Mobile IPv6 protocol. Note that: the latter protocol employed by the evolved packet data gateway (ePDG) element is responsible for secure interworking between cellular and other networks, such as WiFi, LTE metropolitan area networks, and femtocells. In addition, proprietary interfaces and/or protocols may also be utilized.

In this embodiment, the communication processor is further configured to collect global GPS/GNSS satellite data from a global reference network (WWRN) station. Note that: a universal reference network is a ground-based monitoring station with so-called assistance data.

The location engine (signal processing unit and data processing unit) of the LSU will use the assistance data to determine the wireless device location.

Tracking algorithms or trackers provide the ability to predict future positions of multiple moving objects based on the location history and velocity of the individual objects as reported by the sensor system.

In this embodiment, the tracker algorithm utilizes the wireless device (target) position fix and its measurements from the positioning opportunities to continuously estimate the target position and target velocity, including position/velocity confidence. Thus, over time (i.e., multiple positioning occasions), the tracking algorithm will reduce the position/velocity estimation standard deviation, thereby improving positioning accuracy.

In one embodiment, the data processing unit may be configured to correct/smooth the position and/or velocity estimates, for example performing tracking using a kalman filter, a particle filter, an enhanced α - β filter. The enhanced alpha-beta filter uses the position/velocity confidence measure to adjust the alpha/beta value.

However, in another embodiment, the data processing unit may also be configured to generate User Interface (UI) information from the output of the tracker (position fix and its measurements) and other information contained in the position occurrence data.

In this embodiment, all LSU components/elements (such as signal processing units and data processing units, location management processors, communication processors, etc.) may be implemented in software. Listed below are several options for LSU deployment (server software execution):

LSUs may be deployed within the core of a network and/or an operator's IP services network.

LSUs may be deployed on servers of a cellular network at an edge facility of a cloud computing-based centralized RAN (C-RAN) baseband processing, such as evolved 4.5G Mobile Edge Computing (MEC) servers, where the LSUs may be integrated as hosted applications. Note that: the RAN is a radio access network.

3. The LSU is hosted in a core network computing cloud and/or an operator's services network cloud.

LSUs may be deployed outside the core of a network and/or an operator's IP services network, connected to one or more networks.

LSUs may be deployed outside the core of a network and/or an operator's IP services network, are cloud services that are fully hosted and managed, and are connected to one or more networks via secure remote internet connections.

LSUs may be deployed in a cloud RAN architecture, where some LSU components/elements (such as signal data acquisition and data processing units) are instantiated and integrated with or proximate to a virtual eNodeB instance (e.g., deployed on the same cloud processing unit, or deployed in a processing unit that has a direct interface with a cloud processing unit supporting the eNodeB instance).

The LSU may be deployed as a separate entity in any private radio network (such as wireless LAN, citizens broadband radio service and LAA) or integrated with some elements of the radio network.

The LSU may be integrated into the E-SMLC or a variant thereof, or replace some of the functionality of the E-SMLC, as this functionality relates to the location determination of the LSU.

Selection 5 supports: a) current 4G and upcoming 5G cellular wireless network deployments, b) non-cellular network/system deployments, and c) location as a service (LaaS) positioning data transmission, whereby the wireless device acts as a gateway for (LSU) protected physical location data. Thus, this option is an embodiment of the present disclosure.

Further, the embodiment is a heterogeneous multi-network and/or multi-type access node environment. In the case of a multi-network environment, one or more communication networks and/or positioning specific systems exist as completely separate entities, each serving a specific application, e.g. cellular, WLAN, etc., and/or one or more dedicated positioning systems, e.g. GPS/GNSS, terrestrial beacon systems, etc. The case of multiple types of access nodes is known as a HetNet environment. Additionally, environments are supported that combine one or more networks with a HetNet.

Note that: hetnets indicate the use of multiple types of access nodes in a wireless network. Wide area networks may use macrocells, small cells (microcells/picocells/femtocells), and/or DAS to provide wireless coverage in environments with a wide variety of wireless coverage areas, ranging from open outdoor environments to office buildings, homes, and underground areas.

However, there are also special cases of hetnets-Heterogeneous Wireless Networks (HWN). Hetnets may also be comprised of elements/components with different capabilities in terms of operating system, hardware, protocols, etc., while HWN is a wireless network comprised of devices using different underlying Radio Access Technologies (RATs).

The current multi-network and multi-type access node environment is shown in fig. 42. The environment includes LTE and Wi-Fi wireless communication networks plus GPS/GNSS and terrestrial beacon-specific positioning systems.

The various node types include macro cell 4202 withbase station 4203, city cell 4204, small cell: outdoor/campus-4204, indoor-4214; WLAN AP4218, active DAS (indoor/campus) -4230 and passive DAS (indoor only) -4224; terrestrial beacon 4208 and LMU (location measurement/management unit): indoor-4220, outdoor-4210, are also integrated LMUs (not shown) that may reside in thebase station 4203 of the macro cell. It should be noted that the LMUs are not integrated with the DAS Base Station (BS) 4225.

Also depicted in fig. 42 is awireless device 4260, which may also be referred to as a UE, and may be a handheld terminal, a wireless IoT sensor, or a tag. Thewireless device 4260 also receives downlink transmissions from GPS/GNSS satellites 4250.

The current LTE EPC (evolved packet core) is shown in fig. 43A. Figure 43A includes a coreIP services network 4306 of a network capable of managing/supporting location services (LCS) tasks and anentity 4308 of the IP services network; other entities may also be included. TheIP serving network 4306 and theentity 4308 of the IP serving network are not part of the EPC.

According to fig. 43A, the aforementioned nodes are connected with an LTE EPC (evolved packet core) via a backhaul network 4240 (fig. 42). The backhaul in figure 42 includes one or more gateways/aggregation points 4242 that support small cells, urban cells, and Wi-Fi connections to the EPC. For example, the WLAN controller 4216 is connected to the EPC component (PGW 4304) via an ePDG (evolved packet data gateway). The backhaul network may also support connections between nodes and LSUs. TheWWRN 4252, which is part of the GPS/GNSS system, communicates with the EPC component.

As shown in fig. 43A, the backhaul connection for the macrocell is terminated withEPCMME 4305 andSGW 4303 components. Data from the metro cell, small cell and/or small cell controller 4212 then passes through the gateway/aggregation point 4242 to theMME 4305/SGW 4303. Meanwhile, after passing through thebackhaul gateway 4242, data from the WLAN controller 4216 is terminated at thePGW 4304. The LMU's indoor 4220 and/or outdoor 4210 nodes communicate with the E-SMLC 4309 components and theWWRN 4252, which provides the E-SMLC with the necessary satellite/system assistance information, which the E-SMLC then propagates to the wireless devices, or the E-SMLC may also utilize in determining a target location fix in AGPS/AGNSS mode.

Listed below are specific modes of operation of the embodiments. Unlike current positioning architectures, in this embodiment neither E-SMLC 4309 norUE 4260 of the present disclosure perform wireless device position calculations. Similarly, the LMU network elements of the present disclosure also do not perform wireless device location calculations. In addition, neither the UE nor the LMU are calculating observable objects and their metrics. Furthermore, while in the current architecture, assistance/assistance information is collected and transmitted by the E-SMLC, in the architecture of an embodiment, this information is collected and distributed by the LSU of the present disclosure.

With respect to command and control/status message exchanges, they may conform to industry standard interfaces/protocols/procedures, such as OMA SUPL or 3GPP (via E-SMLC), or MQTT-like alternatives. In addition, proprietary interfaces and/or protocols/procedures may be utilized.

1. Downlink positioning：

Downlink positioning occasion data packets (including GPS/GNSS) are sent from the wireless device (by the device's resident logical entity of the present disclosure) to the LSU. These data packets may be transmitted on the LTE user plane by extending LPP (LTE positioning protocol) to carry the positioning occasion information of the present disclosure.

Currently, LPP is used for data exchange between the UE and a network core element, and in case of OMA secure user plane location architecture (SUPL), extended LPP (lppe) is used for data exchange between the UE and SUPL Location Platform (SLP). This communication method (using LPPe) may also be used by the LSU to provide assistance/assistance information for the wireless devices of the present disclosure, if necessary.

Alternatively, the data packets may be transmitted over any other form of secure data bearer, such as Wi-Fi connected to the EPC data (user) plane through a backhaul gateway. Other Wi-Fi options include radio level integration (LWIP) technology with IPsec Tunnel using 3GPP LTE-WLAN aggregation (LWA) or 3GPP LTE with WLAN.

Another option is to exchange data packets between the UE and the LSU via an internet of things (IoT) platform, such as Amazon Web Services (AWS) IoT, google cloud, or AT & T M2 x.

However, another way for a wireless device (UE) of the present disclosure to communicate (exchange) data to (with) an LSU is to use a protocol like MQTT. MQTT is a publish/subscribe instant messaging protocol designed for lightweight machine-to-machine (M2M) communication. The instant messaging protocol described above is used by amazon web services, Azure, and many other cloud-based solutions. Additional options include Advanced Message Queuing Protocol (AMQP), streaming text directed message protocol (stopp), IETF constrained application protocol, XMPP, DDS, OPC UA, and Web Application Message Protocol (WAMP).

One advantage of this publish/subscribe mechanism is that the Location Server (LSU) does not have to know the cellular network UE identifier, such as the UE IMSI or IMEI, or its IP address. Instead, all communications utilize identifiers defined and managed by the location service. Communication may likewise be via 3G, 4G, 5G, and/or Wi-Fi, among others. In fact, the communication need not utilize a cellular network. The communication may use any type of connection that provides an internet connection.

In addition, proprietary interfaces and/or protocols may also be utilized.

2. Uplink positioning：

In the current form of LMUs, LMUs are complex, stand-alone devices that are completely dedicated to receiving and processing uplink reference signals to compute TDOA forms (relative time of arrival (RTOA) observable objects, also known as uplink measurements). While LTEstandard release 11 and later versions where LMUs and enodebs are specified to be integrated, in its current form, LMUs are not easily incorporated into enodebs and/or other devices.

On the other hand, for running the network elements of the present disclosure, the LMU logical entity is a low complexity task (effort) that presents only a small computational load. This enables the LMU logical entity to be easily integrated with all types of enodebs — macro cells, small cells, urban cells, and other devices (e.g., WLAN APs, active DAS head units, etc.). Furthermore, the complexity, cost and power consumption of the standalone (indoor/outdoor) LMUs of the present disclosure are greatly reduced.

Thus, in this embodiment, the uplink positioning occasion data packet is sent from a network element (by the resident logical entity of the LMU) integrated with the LMU of the present disclosure to the LSU. At the same time, uplink positioning occasion data packets are also sent from the independent (indoor/outdoor) LMUs of the present disclosure.

In the current uplink architecture, LMUs exchange data packets with E-SMLC over the SLm interface using the SLm interface application protocol slmapp (see fig. 43A). Thus, the data packets can be transmitted directly to the LSU using the SLm interface and extending the slmaps to carry the positioning occasion information. This communication method may also be used by the LSU to provide assistance/supplementary information for the LMU of the present disclosure, if necessary.

Alternatively, the positioning occasion data packets may be transmitted over another form of secure data bearer over a connection providing an internet connection, such as internet protocol security (IPsec). Further, the uplink positioning occasion data may be sent to the LSU via a wireless device logic entity of the present disclosure. The communication may be via 3G, 4G, 5G, and/or Wi-Fi, among others. The disadvantage of this approach is the overhead (load) of the available communication bandwidth.

Another option is to exchange data packets directly between the LMUs and LSUs of the present disclosure through Amazon Web Services (AWS) IoT, google cloud, AT & T M2x, Azure, and many other cloud-based solutions.

Yet another option is similar to the downlink approach, whereby the LMUs of the present disclosure will employ amazon web services, Azure, and many other cloud-based solutions to exchange data with LSUs by using protocols like MQTT.

With the communication link established between the LMUs and LSUs of the present disclosure, uplink positioning may be achieved with the LMUs of the present disclosure integrated into theeNodeB 4203 of the macro cell, the active DAS head-end unit 4231, thesmall cell 4214/4206, the urban cell 4204, and the WLAN AP 4218.

3. LSU connection of the present disclosure：

Fig. 43B and 43C illustrate the connection of LSU embodiments. In this embodiment, it is assumed that all communications with the LSU are over the internet and that the connection is secure, such as over IPsec. Other options may also be used (see downlink and uplink description above).

In one embodiment, all of the positioning functions belong to a single entity LSU located outside the network core (EPC) and/or the coreIP services network 4306. Thus, as described in the embodiments, all of the signal processing, localization, tracking, and navigation are performed in the LSU. Furthermore, in the architecture of this embodiment, assistance/assistance information is also collected and distributed by the LSU, including data from the WWRN received via the backhaul network. Note that: the WWRN is part of a GPS/GNSS system.

The LSU also exchanges location opportunity data packets and other information with one or more independent LMUs of the present disclosure and/or one or more integrated LMUs of the present disclosure. At the same time, the LSU exchanges positioning occasion data packets and other information with one or more wireless devices of the present disclosure.

Here, data packets and other information exchanges may be carried over IP protocols and in proprietary formats. Alternatively, data packets and other information may be exchanged via platforms such as Amazon Web Services (AWS), google cloud, AT & T M2x, Azure, and many other cloud-based solutions. In addition, protocols such as MQTT may also be used for data packets and other information exchange. Other options include Advanced Message Queuing Protocol (AMQP), streaming text directed message protocol (stopp), etc. Additionally, proprietary interfaces and/or protocols may be utilized.

In one embodiment (fig. 43C), all of the above LSU data packet and other information exchanges may be performed via the IPserving network entity 4308.

In another embodiment (fig. 43B), the communication link between one or more LMUs and LSUs and other information sources does not use the IPserving network entity 4308. Thus, the backhaul network may also support a direct connection between one or more network nodes and the LSU of the present disclosure.

In this embodiment,unit 4308 will transmit data packets and other information exchanges between the LSU of the present disclosure and the mobile device and/or one or more network elements (e.g., LMUs) of the present disclosure. There are several ways to guide this information exchange, for example using protocol tunneling techniques.

The specific implementation of the IPserving network entity 4308 is MNO related. An MNO is a mobile network operator, also known as a wireless service provider, wireless operator, cellular company, etc. Thus, the LSU andunit 4308 command/control communication interfaces of the present disclosure may be MNO specific.

4.E-SMLC：

In this embodiment, the LSU of the present disclosure essentially takes over theE-SMLC 4309 functionality with the possible exception of command and control/status message exchanges that may be performed via the E-SMLC in accordance with 3GPP standard interfaces/protocols/procedures. Also, these tasks may be performed by way of an OMA SUPL based approach or by employing an alternative scheme (e.g., MQTT protocol). In addition, proprietary interfaces and/or protocols/procedures may be used for this purpose. Thus, it is not necessary to employ E-SMLC for command and control/status message exchanges.

Another possible exception is when wireless device positioning requires assistance/assistance information from one or more enodebs. Note that: the E-SMLC uses LTE positioning protocol a (lppa) to obtain this information. Also, the LSU may be configured to obtain this information in other ways.

However, during a phased implementation, some of the E-SMLC services may need to be utilized. This may be achieved by including E-SMLC, logical entities of the present disclosure, andrelay entity 4310 in fig. 43B, 43C, and 43D into a network element, which is configured to collect and store the aforementioned assistance/assistance information. The relay entity may also be configured to deliver the information to the LSU. Further, the relay logic entity may be further configured to support the above-described command and control/status message exchange performed according to the 3GPP standard via the E-SMLC.

The relay entity and LSU may communicate via an IPserving network entity 4308 consistent with the LSU connection of the present disclosure described insection 3 above, or directly without the IPserving network entity 4308 by the same means/methods explained insection 3 above. In addition, proprietary interfaces and protocols may be used.

Based on the above description:

1. in one embodiment, the LSU location management processor may be configured to obtain assistance/assistance information from one or more enodebs with or without interaction with the E-SMLC.

2. In one embodiment, the LSU location management processor may be configured to support command and control/status message exchanges according to 3GPP standards with or without interaction with the E-SMLC.

However, in one embodiment, the LSU location management processor of the present disclosure may be configured to exchange positioning occasion data packets and other information via the E-SMLC with all of the one or more LMUs of the present disclosure and the one or more wireless devices of the present disclosure and assistance/assistance information; and to support command and control/status message exchanges in the case of interaction with the E-SMLC.

This embodiment is shown in fig. 43D and will utilize the relay entity described above, which is configured to collect all and other information in the positioning occasion data packets, including assistance/assistance information to be sent to the LSU. The relay entity will also be responsible for communication between the LSU and E-SMLC of the present disclosure.

According to this embodiment, the communication protocol currently utilized (by E-SMLC) may be extended to support the positioning occasion formats (data) of the present disclosure, including LPP, SLmAP, and LPPa protocols.

According to this embodiment, all communication between the relay logic entity and the LSU of the present disclosure is over the internet and the connection is secure. Other options may also be used (see description above). In addition, proprietary interfaces and protocols may be used.

According to this embodiment, the relay entity and LSU of the present disclosure may communicate via an IPserving network entity 4308 consistent with the LSU connection of the present disclosure described insection 3 above, or directly without the IPserving network entity 4308 by the same means/methods explained insection 3.

In another embodiment, the positioning engine (signal processing and data processing unit) functionality of the LSU of the present disclosure is incorporated with the E-SMLC. According to this embodiment, the LSU function is performed in the EPC component, E-SMLC.

An embodiment of a unified framework/platform is shown in fig. 44. It is a multi-network and multi-type access node environment, including LTE and Wi-Fi wireless communication networks plus GPS/GNSS and terrestrial beacon-specific positioning systems.

Unlike the current environment shown in fig. 42, this embodiment supports the LMU integration of the present disclosure into theeNodeB 4403 of the macro cell, the active DAS (4430) head end unit 4431, the small cell: indoor 4414 (small cell controller 4412) and outdoor 4406, urban cell 4404 and WLAN AP 4418(WLAN controller 4416), and standalone LMUs of the present disclosure: indoor 4120 and outdoor 4110. It should be noted that the LMUs of the present disclosure are not integrated with the DAS Base Station (BS) 4425.

Also depicted in fig. 44 is awireless device 4460, which may also be referred to as a UE, and which may be a handheld terminal, a wireless IoT sensor, or a tag. The IOT sensors may be embedded in the object to be tracked. Thewireless device 4460 also receives downlink transmissions from GPS/GNSS satellites 4450.

Communication between these elements and the LSU of the present disclosure occurs through backhaul 4440 and outside of the EPC (i.e., without any EPC elements participating). These communication links are shown in blue.

Additionally, communication (data) between the global reference network (WWRN 4452) and the LSU of the present disclosure occurs through backhaul 4440 and outside of the EPC. The communication link is also shown in blue.

In light of the above disclosure, a network-centric architecture has been described that supports LaaS data transport and is designed for 5G and other networks when all signal processing and location estimation is done in the cloud (i.e., outside the EU and/or eNodeB). There are many options for how all signal processing and position estimation can be done. In terms of uplink positioning, relevant UE reference signal transmissions may be collected/pre-processed by the eNodeB and forwarded to a positioning server unit (LSU) for further processing and UE position determination. In the case of downlink (OTDOA), the tasks of collecting and pre-processing downlink reference signals may be performed by the UE. The UE may then send the collected downlink data to the LSU. In the case of downlink (OTDOA), the UE may also use the control plane and/or LTE user plane to handle communications with the LSU. Thus, the signaling may conform to the OMA Secure User Plane Location (SUPL) protocol and/or 3GPP, such as the LTE Positioning Protocol (LPP). In the case of uplink AoA/DoA, the eNodeB may use LPPA and slmapp protocols (SLm application protocol) to handle communications with LSUs. In addition, proprietary interfaces and/or protocols may also be utilized.

In light of the foregoing, this network-centric architecture may enable advanced features not previously feasible. These advanced features include: (a) determining the distance between the serving cell/tower and the UE using downlink OTDOA and determining the UE position using uplink AoA/DoA while also mitigating the impact of OTDOA synchronization error on the uplink/downlink UE positioning by using tracking algorithms/techniques; (b) using tracking algorithms/techniques to improve UE positioning in the case that the uplink AoA/DoA plus RTT method is used to determine UE position; and (c) utilizing tracking algorithms/techniques to estimate and correct/reduce synchronization errors in downlink OTDOA positioning methods.

In accordance with the above, a downlink (OTDOA) UE positioning method is also described, where the navigation processor is utilizing a multipoint positioning technique/method, also known as hyperbolic navigation. This multipoint location technique requires solving a plurality of hyperbolic (RSTD/TDOA) equations. There are iterative methods and non-iterative (closed form) solutions. In one embodiment, a hybrid method is described that separates a plurality of available (audible) reference points (base stations) into sets of three RSTD/TDOA subsets and finds a closed-form solution for each subset. Thereafter, the location fix may be completed using a location consistency algorithm. In a second embodiment, a combination of iterative and non-iterative solutions of RSTD/TDOA values from the same group may be utilized to improve location bearing. In a third embodiment, an initial position estimate for the UE position may be determined based on iterative and non-iterative algorithms by utilizing the uplink AoA/DoA estimate and RTT.

In light of the above, a wireless network environment has been described that sometimes does not support a multipoint positioning method, which requires at least three reference points (for 2-D positioning) to obtain a positioning fix, since only two high power cells are used to transmit RF signals to a large area. Thus, a method is described: when RTT is available, the UE is positioned along an arc defined by the serving sector azimuth beam width and RTT/2 range. A method is also described: when AoA/DoA estimates are available, the UE location may be determined near the intersection of the hyperbola and the arc. Both methods may also be used. When neither RTT nor AoA/DoA estimates are available, the UE location is determined by scoring the intersection points corresponding to each cell/tower (sector) on the selected hyperbola 3608 (see fig. 36). The score is based on the angular difference between the direction the cell points and the direction to the point on the hyperbola and the distance from each point to the corresponding cell/tower. The scores may be weighted according to the SNR of their corresponding cell/tower.

In accordance with the foregoing, the LSU may include a communications processor configured for signaling and information exchange with the UE, eNodeB, and/or network element. The signaling may conform to the OMA SUPL protocol and/or 3GPP LPP/LPPa, or a combination of LPP, LPPa, and SUPL, as well as other protocols used or available for communication with the network, such as the LCS-AP protocol. In addition, proprietary interfaces and/or protocols may also be utilized.

In accordance with the foregoing, the LSU component may be instructions stored in a memory and configured to execute on a processor of a 4.5G MEC (mobile edge computing) server located at an edge of a communication network. The LSU component may be integrated as a managed application on a 4.5G MEC. In a 5G deployment, LSU components may be hosted in a core network computing cloud. LSUs hosted in the core network computing cloud may support LaaS data transport, whereby the UE acts as a gateway for the core network computing cloud and LaaS, dedicated to protected physical location data.

As described above, the LSU may include a downlink signal processor as well as an uplink signal processor and a navigation processor.

Having thus described various embodiments of the systems and methods, it should be apparent to those skilled in the art that certain advantages of the methods and apparatus have been achieved. In particular, those skilled in the art will appreciate that a system for tracking and locating objects may be assembled at very small incremental cost using FGPA or ASIC and standard signal processing software/hardware combinations. Such systems may be used in a variety of applications, such as locating people in indoor or outdoor environments, harsh and hostile environments, and the like.

It should also be understood that various modifications, adaptations, and alternative embodiments thereof may be made within the scope and spirit of the present disclosure.

Claims (27)

Translated fromChinese

1.一种用于确定无线移动设备在异构无线通信环境中的位置的方法，所述异构无线通信环境包括蜂窝网络、广域区域网络、独立专用网络、卫星网络和陆地无线电网络中的一者或多者，所述方法包括：1. A method for determining the location of a wireless mobile device in a heterogeneous wireless communication environment comprising cellular networks, wide area area networks, standalone private networks, satellite networks and terrestrial radio networks One or more, the method includes:在检测器处：At the detector:从一个或多个接收信道接收包括用于所述无线移动设备的定位信号的参数的信号，其中所述定位信号的所述参数描述所述信号内的预先已知信号；receiving a signal from one or more receive channels including parameters of a positioning signal for the wireless mobile device, wherein the parameters of the positioning signal describe a pre-known signal within the signal;接收第一元数据，所述第一元数据包括所述信号的每个帧起始以及第一协助数据和第一辅助信息中的一者或多者；receiving first metadata including the start of each frame of the signal and one or more of first assistance data and first assistance information;基于所述定位信号的所述参数来从所述一个或多个接收信道中的每个接收信道检测所述预先已知信号；detecting the pre-known signal from each of the one or more receive channels based on the parameter of the positioning signal;以数字格式缓冲从所述预先已知信号中提取的多个样本；buffering a plurality of samples extracted from the pre-known signal in digital format;以数字格式压缩所述多个样本以减小所述多个样本的数据大小；compressing the plurality of samples in a digital format to reduce a data size of the plurality of samples;将所述压缩数据、所述定位信号的所述参数和所述元数据发送到物理上远离所述检测器的定位服务器；sending the compressed data, the parameters of the positioning signal, and the metadata to a positioning server that is physically remote from the detector;在所述定位服务器处：At the location server:从所述检测器接收所述压缩数据、所述定位信号的所述参数和所述元数据；receiving the compressed data, the parameters of the positioning signal, and the metadata from the detector;接收所述异构无线通信环境的参数；receiving parameters of the heterogeneous wireless communication environment;从所述异构无线通信环境接收包括第二协助数据和第二辅助信息中的一者或多者的第二元数据；receiving second metadata including one or more of second assistance data and second assistance information from the heterogeneous wireless communication environment;基于所述压缩数据、所述异构无线通信环境的所述参数、所述第二元数据并且基于所述第二协助数据和所述第二辅助信息中的一者或多者来计算所述异构无线通信环境的附加参数；The computing is based on the compressed data, the parameters of the heterogeneous wireless communication environment, the second metadata, and based on one or more of the second assistance data and the second assistance information Additional parameters for heterogeneous wireless communication environments;基于所述压缩数据、所述定位信号的所述参数、所述异构无线通信环境的所述参数、所述附加参数、所述第一元数据和所述第二元数据来计算所述无线移动设备的所述位置；The wireless communication is calculated based on the compressed data, the parameters of the positioning signal, the parameters of the heterogeneous wireless communication environment, the additional parameters, the first metadata and the second metadata said location of the mobile device;执行对所述无线移动设备的跟踪和导航；以及performing tracking and navigation of the wireless mobile device; and向所述异构无线通信环境报告所述跟踪和导航的结果。Results of the tracking and navigation are reported to the heterogeneous wireless communication environment.2.根据权利要求1所述的方法，其中所述第一辅助信息包括一个或多个传感信息和传感器数据。2. The method of claim 1, wherein the first auxiliary information includes one or more of sensory information and sensor data.3.根据权利要求1所述的方法，其中所述定位服务器是位置管理处理器，所述位置管理处理器被配置为执行以下操作中的一者或多者：3. The method of claim 1, wherein the location server is a location management processor configured to perform one or more of the following:与所述无线移动设备和所述异构无线通信环境进行交互；interacting with the wireless mobile device and the heterogeneous wireless communication environment;与定位引擎进行交互；interact with the location engine;管理所述无线移动设备和所述异构无线通信环境与所述定位服务器之间的消息；managing messages between the wireless mobile device and the heterogeneous wireless communication environment and the location server;管理向所述无线移动设备和所述异构无线通信环境的数据传送；以及managing data transfer to the wireless mobile device and the heterogeneous wireless communication environment; and提供安全性。Provide security.4.根据权利要求3所述的方法，还包括：4. The method of claim 3, further comprising:在所述定位引擎处：At the location engine:从所述位置管理处理器接收所述压缩数据、所述定位信号的所述参数、所述异构无线通信环境的所述参数、所述附加参数、所述第一元数据和所述第二元数据；The compressed data, the parameters of the positioning signal, the parameters of the heterogeneous wireless communication environment, the additional parameters, the first metadata and the second received from the location management processor metadata;通过利用从所述位置管理处理器接收的数据来执行位置估计和位置跟踪计算；performing position estimation and position tracking calculations by utilizing data received from the position management processor;利用从自所述位置管理处理器接收的所述数据导出的上行链路可观察对象和下行链路可观察对象中的一者或多者来执行位置估计；performing location estimation utilizing one or more of uplink observables and downlink observables derived from the data received from the location management processor;计算位置相关量度，所述位置相关量度包括置信度半径值、几何精度因子和无线E911位置准确度中的一者或多者；以及calculating a location-related measure including one or more of a confidence radius value, a geometrical precision factor, and a wireless E911 location accuracy; and向所述位置管理处理器提供所述位置估计和位置跟踪计算、所述位置估计和位置相关量度。The position estimates and position tracking calculations, the position estimates and position related metrics are provided to the position management processor.5.根据权利要求4所述的方法，其中所述定位引擎连续地向提供给所述位置管理处理器的所述位置估计和位置跟踪计算、所述位置估计和位置相关量度提供更新。5. The method of claim 4, wherein the positioning engine continuously provides updates to the position estimates and position tracking calculations, the position estimates and position related metrics provided to the position management processor.6.根据权利要求3所述的方法，其中所述定位引擎是导航处理器。6. The method of claim 3, wherein the positioning engine is a navigation processor.7.根据权利要求3所述的方法，其中所述定位引擎包括信号处理单元和数据处理单元，所述方法还包括：7. The method of claim 3, wherein the positioning engine includes a signal processing unit and a data processing unit, the method further comprising:在所述信号处理单元处，接收并处理所述压缩数据、所述定位信号的所述参数、所述异构无线通信环境的所述参数、所述附加参数、所述第一元数据和所述第二元数据，以生成包括可观察对象的信号处理单元输出；At the signal processing unit, the compressed data, the parameters of the positioning signal, the parameters of the heterogeneous wireless communication environment, the additional parameters, the first metadata and the the second metadata to generate a signal processing unit output comprising an observable;在所述位置管理处理器处，从所述数据处理单元接收位置估计和位置跟踪计算、所述位置估计和位置相关量度；以及at the location management processor, receiving from the data processing unit a location estimate and location tracking calculation, the location estimate and location related metrics; and在所述数据处理单元处，使用所述信号处理单元输出来执行所述无线移动设备的定位和跟踪，并根据所述定位和跟踪生成量度元数据。At the data processing unit, positioning and tracking of the wireless mobile device is performed using the signal processing unit output, and metric metadata is generated from the positioning and tracking.8.根据权利要求3所述的方法，其中所述协助信息和/或所述辅助信息也由LSU收集和分发，所述协助信息和/或所述辅助信息包括经由回程网络从全球参考网络(WWRN)站接收的数据，其中所述WWRN是GPS和/或GNSS系统的一部分。8. The method of claim 3, wherein the assistance information and/or the assistance information is also collected and distributed by the LSU, the assistance information and/or the assistance information comprising data from a global reference network ( Data received by a WWRN) station, where the WWRN is part of a GPS and/or GNSS system.9.根据权利要求3所述的方法，其中所述位置管理处理器包括通信处理器，所述通信处理器被配置用于：9. The method of claim 3, wherein the location management processor comprises a communications processor configured to:与一个或多个通信网络和/或定位系统的无线设备(UE)进行信令、数据和信息交换；Exchange of signaling, data and information with wireless equipment (UE) of one or more communication networks and/or positioning systems;与一个或多个所述网络和/或定位系统进行信令、数据和信息交换，包括交换LCS和来自全球参考网络(WWRN)站的全球GPS/GNSS卫星数据，所述一个或多个所述网络和/或定位系统包括一个或多个所述网络和/或定位系统的元件；以及Signaling, data and information exchange, including exchange of LCS and global GPS/GNSS satellite data from Global Reference Network (WWRN) stations, with one or more of said networks and/or positioning systems, said one or more of said a network and/or positioning system comprising one or more elements of said network and/or positioning system; and利用一个或多个联网协议、接口、计算平台和/或专有协议和接口。Utilize one or more networking protocols, interfaces, computing platforms and/or proprietary protocols and interfaces.10.根据权利要求7所述的方法，其中所述信号处理单元可被配置为：10. The method of claim 7, wherein the signal processing unit is configurable to:估计来自一个或多个通信网络和/或一个或多个定位系统的下行链路信号和/或上行链路信号的位置可观察对象及其量度，其中所述位置可观察对象包括飞行时间(TOF)、到达时间(TOA)、到达时间差(TDOA)、到达角(AOA)和到达方向(DOA)；以及Estimating position observables and metrics of downlink signals and/or uplink signals from one or more communication networks and/or one or more positioning systems, wherein the position observables include time of flight (TOF ), Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA) and Direction of Arrival (DOA); and采用多路径抑制处理器以增强所述可观察对象的结果准确度，所述多路径抑制处理器包括美国专利号7872583中所述的方法、技术和算法。A multipath suppression processor is employed to enhance the accuracy of the results for the observable object, the multipath suppression processor including the methods, techniques and algorithms described in US Pat. No. 7,872,583.通过利用包括若干方法、技艺和技术的组合的包括所有的信号处理方法、技艺和技术，利用混合信号和传感信息来执行所述位置可观察对象的计算。The computation of the position-observable object is performed using mixed signal and sensory information by utilizing signal processing methods, techniques and techniques, including all, including combinations of several methods, techniques and techniques.11.根据权利要求3所述的方法，其中包括信号和数据处理单元、位置管理处理器、通信处理器的所述LSU和/或所述LSU部件可部署在以下的一者或多者中：11. The method of claim 3, wherein the LSU and/or the LSU components comprising a signal and data processing unit, a location management processor, a communications processor may be deployed in one or more of:网络和/或运营商的IP服务网络的核心；the core of the network and/or the operator's IP service network;基于云计算的集中式无线电接入网络(RAN)(C-RAN)基带处理的边缘设施处的蜂窝网络的服务器，所述服务器包括演进型4.5G移动边缘计算(MEC)服务器，其中所述LSU和/或LSU部件可被集成为托管应用程序；A server for a cellular network at an edge facility for cloud-based Centralized Radio Access Network (RAN) (C-RAN) baseband processing, the server including an Evolved 4.5G Mobile Edge Computing (MEC) server, wherein the LSU and/or LSU components may be integrated into hosted applications;在核心网络计算云和/或运营商的服务网络云中，即托管在云中；In the core network computing cloud and/or the operator's service network cloud, i.e. hosted in the cloud;在网络和/或运营商的IP服务网络的核心的外部，连接到一个或多个网络；outside the core of the network and/or the operator's IP service network, connected to one or more networks;在网络和/或运营商的IP服务网络的核心的外部，是被完全托管和管理的云服务，并且经由安全远程互联网连接被连接到一个或多个网络；Outside the core of the network and/or the operator's IP service network, is a fully hosted and managed cloud service and connected to one or more networks via a secure remote Internet connection;在云RAN架构中，其中一些LSU部件与虚拟eNodeB实例集成或并置排列，被部署在同一云处理单元上或被部署在具有到支持所述eNodeB实例的所述云处理单元的直接接口的处理单元中；In a cloud RAN architecture, some of the LSU components are integrated or collocated with a virtual eNodeB instance, deployed on the same cloud processing unit or in a process with a direct interface to the cloud processing unit supporting the eNodeB instance in the unit;在包括无线LAN、市民宽带无线电服务和授权辅助接入(LAA)的任何专用无线电网络中作为独立实体或与所述无线电网络的一些元件集成；as a stand-alone entity or integrated with some element of the radio network in any private radio network including wireless LAN, Citizen Broadband Radio Service and Licensed Assisted Access (LAA);在E-SMLC中，其中完全集成或在所述E-SMLC的功能中的一些功能涉及所述LSU的位置确定时替换所述一些功能。In the E-SMLC, some of the functions of the E-SMLC are either fully integrated or replaced when some of the functions of the E-SMLC relate to the location determination of the LSU.12.根据权利要求11所述的方法，其中所述LSU可被配置为：12. The method of claim 11, wherein the LSU is configurable to:在当前4G和即将到来的5G蜂窝无线网络部署中操作；Operates in current 4G and upcoming 5G cellular wireless network deployments;在非蜂窝网络和/或系统中操作；operate in non-cellular networks and/or systems;在位置即服务(LaaS)定位数据传输中操作，其中无线设备充当(所述LSU)受保护的物理位置数据的网关；operate in a location-as-a-service (LaaS) positioning data transfer, where the wireless device acts as a gateway to (the LSU) protected physical location data;在所述UE和/或eNodeB的外部操作，其中所有信号处理和位置估计在所述云中执行；operating externally to the UE and/or eNodeB, with all signal processing and location estimation performed in the cloud;在所述网络的核心和/或核心IP服务网络的外部操作，其中所述信号处理、定位、跟踪和导航中的全部在所述LSU中执行。Operates outside the core of the network and/or the core IP service network, with all of the signal processing, positioning, tracking and navigation performed in the LSU.13.根据权利要求11所述的方法，其中所述LSU可被配置为：13. The method of claim 11, wherein the LSU is configurable to:在异构多网络中操作，其中一个或多个通信网络和/或定位特定系统作为完全独立的实体存在，每个实体服务于包括蜂窝和WLAN和/或GPS/GNSS和/或陆地信标专用定位系统的特定应用；Operates in heterogeneous multi-networks, where one or more communication networks and/or positioning-specific systems exist as completely separate entities, each serving a dedicated service including cellular and WLAN and/or GPS/GNSS and/or terrestrial beacons specific applications of positioning systems;在包括HetNet环境的多种类型的接入节点环境中操作，其中HetNet是用于由不同小区类型和不同接入技术的组合构成的通信网络的术语；operate in multiple types of access node environments including HetNet environments, where HetNet is a term used for communication networks consisting of a combination of different cell types and different access technologies;在将一个或多个网络与所述HetNet组合的环境中操作。Operates in the context of combining one or more networks with the HetNet.14.根据权利要求1所述的方法，其中所述无线设备和/或网络的元件能够包括检测器，其中所述检测器被配置为：14. The method of claim 1, wherein an element of the wireless device and/or network can include a detector, wherein the detector is configured to:检测和提取用于设备定位的信号的数字样本；Detect and extract digital samples of signals used for device location;收集每个天线和每个信号标识(ID)的多个数字样本；Collect multiple digital samples of each antenna and each signal identification (ID);收集和存储包括每个帧起始的元数据以及辅助信息和/或协助信息；collect and store metadata including the start of each frame and auxiliary and/or assisting information;预处理所收集的数字样本以在被发送到所述LSU之前减小所述数据大小，其中所述数据大小减小能够包括提取仅表示用于设备定位的所述信号的数字样本；以及preprocessing the collected digital samples to reduce the data size prior to being sent to the LSU, wherein the data size reduction can include extracting digital samples representing only the signal used for device positioning; and收集所述GPS/GNSS导航消息帧中的数据样本子集，所述数据样本子集是所述消息的一小部分，collecting a subset of data samples in the GPS/GNSS navigation message frame, the subset of data samples being a small portion of the message,其中所述导航消息中携带的信息的其余信息将能够在所述LSU处用作所述GPS和/或GNSS协助数据，并且wherein the remainder of the information carried in the navigation message will be available at the LSU as the GPS and/or GNSS assistance data, and其中所述协助数据包括参考时间、参考位置、卫星星历、时钟校正、电离层模型、地球取向参数、GNSS时间偏移、采集协助、Almanac、UTC模型等。The assistance data includes reference time, reference position, satellite ephemeris, clock correction, ionospheric model, earth orientation parameters, GNSS time offset, acquisition assistance, Almanac, UTC model, and the like.15.根据权利要求14所述的方法，其中所述检测器能够与所述无线设备和/或网络的元件集成或并置排列，并且其中所述检测器从所述无线设备(UE)的所述接收器接收所述信号的样本和其他相关信息。15. The method of claim 14, wherein the detector is capable of being integrated or collocated with elements of the wireless device and/or network, and wherein the detector is obtained from all elements of the wireless device (UE) The receiver receives samples of the signal and other relevant information.16.根据权利要求14所述的方法，其中所述网络的元件能够是LMU和/或eNodeB或两者的组合。16. The method of claim 14, wherein the elements of the network can be LMUs and/or eNodeBs or a combination of both.17.根据权利要求14所述的方法，其中所述检测器包括通信处理器，并且其中所述处理器被配置为：17. The method of claim 14, wherein the detector comprises a communications processor, and wherein the processor is configured to:组织样本数据；tissue sample data;利用一个或多个联网协议、接口、计算平台和/或专有协议和接口来进行信号传递以及与所述LSU交换数据和信息；以及utilize one or more networking protocols, interfaces, computing platforms and/or proprietary protocols and interfaces for signaling and exchanging data and information with the LSU; and被配置为将样本数据和元数据发送到所述LSU并接收命令和协助信息。is configured to send sample data and metadata to the LSU and to receive commands and assistance information.18.根据权利要求14所述的方法，其中所述通信处理器被配置为使用所述不同小区类型和/或一种或多种接入技术来将数据和信息发送回所述LSU，所述一种或多种接入技术包括蜂窝、WLAN、蓝牙、ZigBee和IEEE 802.15无线技术。18. The method of claim 14, wherein the communications processor is configured to transmit data and information back to the LSU using the different cell types and/or one or more access technologies, the The one or more access technologies include cellular, WLAN, Bluetooth, ZigBee, and IEEE 802.15 wireless technologies.19.根据权利要求14所述的方法，其中所述设备(UE)的数据信道被分配在较大带宽信道的频带中(在所述较大带宽信道内)，并且其中所述设备的接收器能够在所分配信道的带宽之外访问用于所述设备定位的信号；所述检测器被配置为处理所分配的信道带宽之内和之外的信号，并将所得的样本数据和其他相关信息发送到所述LSU。19. The method of claim 14, wherein a data channel of the device (UE) is allocated in a frequency band of a larger bandwidth channel (within the larger bandwidth channel), and wherein a receiver of the device (UE) Signals used for positioning of the device can be accessed outside the bandwidth of the allocated channel; the detector is configured to process signals within and outside the bandwidth of the allocated channel, and integrate the resulting sample data and other relevant information sent to the LSU.20.根据权利要求14所述的方法，其中所述检测器被配置为：20. The method of claim 14, wherein the detector is configured to:检测和提取用于设备(UE)定位的概率统计值，例如数据；以及Detect and extract probabilistic statistics, such as data, for equipment (UE) positioning; and将这些概率统计值与其元数据和相关联的辅助信息和/或协助信息分组。These probability statistics are grouped with their metadata and associated auxiliary information and/or assistance information.21.根据权利要求14所述的方法，其中所述检测器被配置为实现进一步的数据大小减小：21. The method of claim 14, wherein the detector is configured to achieve further data size reduction:利用一种或多种压缩算法，所述压缩算法包括A-定律和U-定律复合算法；utilizing one or more compression algorithms including A-law and U-law composite algorithms;将每个数字样本大小减小到更少的位数；reduce each digit sample size to fewer digits;采用相干压缩方案，其中将用于可观察对象估计(发送到LSU)的所得数据样本的大小减小到在所述接收器信号互相关输出处捕获的非零部分乘以所检测到的唯一ID(标识符)信号的数量；A coherent compression scheme is employed in which the size of the resulting data samples for observable estimation (sent to the LSU) is reduced to the non-zero fraction captured at the receiver signal cross-correlation output times the detected unique ID (identifier) number of signals;采用压缩方案，其中在成功检测到用于设备定位的信号之后，进一步的操作包括根据所接收信号的ID结合载波频率偏移(CFO)处理进行匹配滤波；随后(在时间上)对这些所接收信号的多个样本进行积分。A compression scheme is employed in which, after successful detection of a signal for device location, further operations include matched filtering based on the ID of the received signal in combination with carrier frequency offset (CFO) processing; these received Multiple samples of the signal are integrated.22.根据权利要求21所述的方法，其中所述检测器被配置为执行附加压缩处理以减小数据大小，包括对用于设备定位的信号进行匹配滤波、计算由所述信号的数字样本中的一个或多个数字样本形成的矩阵的奇异值分解(SVD)原理本征值，其中所述附加处理是计算负载和/或所需资源与减少与所述LSU的通信带宽之间的权衡。22. The method of claim 21, wherein the detector is configured to perform additional compression processing to reduce data size, including matched filtering of the signal used for device location, calculating the Singular value decomposition (SVD) principle eigenvalues of a matrix formed by one or more digital samples of , wherein the additional processing is a trade-off between computational load and/or required resources and reducing communication bandwidth with the LSU.23.根据权利要求1所述的方法，其中所述检测器和所述LSU被配置为：23. The method of claim 1, wherein the detector and the LSU are configured to:利用包括OMA SUPL、3GPP LPP、LPPa或LPP、LPPa、SUPL和/或LCS-AP的组合的LTE接口和协议来传输通信内容，所述通信内容包括样本数据、元数据、辅助信息和/或协助信息、信令和/或命令；Utilize LTE interfaces and protocols including OMA SUPL, 3GPP LPP, LPPa or a combination of LPP, LPPa, SUPL and/or LCS-AP to transmit communications including sample data, metadata, assistance information and/or assistance information, signaling and/or commands;利用包括3GPP LTE-WLAN聚合(LWA)或3GPP LTE与WLAN通过IPsec Tunnel的无线电级集成(LWIP)技术的WLAN承载来传输通信内容；Utilize WLAN bearer including 3GPP LTE-WLAN aggregation (LWA) or 3GPP LTE and WLAN over IPsec Tunnel Radio Level Integration (LWIP) technology to transmit communication content;利用消息队列遥测传输(MQTT)和/或另选的连接协议经由包括亚马逊网络服务(AWS)、谷歌云、AT&T M2x的计算平台来传输所述通信内容；using Message Queuing Telemetry Transport (MQTT) and/or an alternative connection protocol to transmit said communications via computing platforms including Amazon Web Services (AWS), Google Cloud, AT&T M2x;通过包括互联网协议安全(IPsec)、具有IKEv2的IPsec和代理移动IPv6的任何替代形式的安全承载来传输所述通信内容；以及Transport the communication content over any alternative form of security bearer including Internet Protocol Security (IPsec), IPsec with IKEv2, and Proxy Mobile IPv6; and通过专有接口和/或协议来传输所述通信内容。The communication content is transmitted through proprietary interfaces and/or protocols.24.根据权利要求23所述的方法，其中所述演进分组数据网关(ePDG)用于所述LTE和需要安全接入的非3GPP网络之间的互通，所述非3GPP网络包括WLAN、LTE城域网和毫微微小区接入网络。24. The method of claim 23, wherein the evolved packet data gateway (ePDG) is used for interworking between the LTE and non-3GPP networks requiring secure access, the non-3GPP networks including WLAN, LTE Area network and femtocell access network.25.根据权利要求11所述的方法，其中所述LSU位于网络和所述网络的IP服务的外部，其中所述设备(UE)定位在所述网络元件中的任何网络元件的外部确定，并且其中所述E-SMLC采用中继单元，所述中继单元被配置为：25. The method of claim 11, wherein the LSU is located external to a network and IP services of the network, wherein the location of the equipment (UE) is determined external to any of the network elements, and Wherein the E-SMLC adopts a relay unit, and the relay unit is configured as:在所述检测器和所述LSU之间传输样本数据、元数据、辅助信息和/或协助信息、信令和/或命令；transmitting sample data, metadata, auxiliary information and/or assistance information, signaling and/or commands between the detector and the LSU;利用SUPL Llp接口和底层协议或经由专有接口和/或协议。Utilize the SUPL Llp interface and underlying protocols or via proprietary interfaces and/or protocols.26.根据权利要求25所述的方法，其中所述LMU使用扩展的SLm-AP协议通过所述SLm接口与E-SMLC和中继实体进行通信以适应所述LMU和所述中继通信和传输。26. The method of claim 25, wherein the LMU communicates with an E-SMLC and a relay entity over the SLm interface using an extended SLm-AP protocol to accommodate the LMU and the relay communication and transmission .27.根据权利要求25所述的方法，其中所述中继单元具有对包括样本和协助数据的所有定位相关信息的访问权限。27. The method of claim 25, wherein the relay unit has access to all positioning related information including samples and assistance data.

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