技术领域Technical Field
本发明涉及室内定位技术领域，特别是涉及一种室内移动目标定位方法及系统。The present invention relates to the field of indoor positioning technology, and in particular to an indoor mobile target positioning method and system.
背景技术Background Art
随着现代通信技术的改革创新，以及更多新型移动设备比如手机、平板电脑、可穿戴设备等物联网设备性能的飞速增长，人类进入万物互联时代，许多基于位置感知功能的应用如室内外导航、商品导购、抢险救援、医疗监护等在人们的日常生活中发挥着越来越重要的作用，人们对定位服务的需求不断增加，而一切基于位置感知应用的基础便是能连续可靠地为其提供位置信息，即可以在多种复杂环境下获取设备或用户的位置。With the reform and innovation of modern communication technology and the rapid growth of the performance of more new mobile devices such as mobile phones, tablets, wearable devices and other IoT devices, human beings have entered the era of the Internet of Everything. Many applications based on location awareness functions, such as indoor and outdoor navigation, product shopping guides, emergency rescue, medical monitoring, etc., are playing an increasingly important role in people's daily lives. People's demand for positioning services is constantly increasing, and the basis of all location-aware applications is the ability to continuously and reliably provide them with location information, that is, to obtain the location of devices or users in a variety of complex environments.
室外定位和基于位置的服务已经成熟，基于全球卫星导航定位系统和地图的位置服务被广泛应用，其原理是利用用户终端与已知卫星之间的距离来测量用户的准确位置信息，可以很好地满足室外环境下的定位需求。对于室外场景，美国的全球定位系统(GPS)、俄罗斯的格洛纳斯系统(GLONASS)、欧盟的伽利略(Galileo)和中国的北斗卫星导航系统(BDS)己覆盖全球，相关导航定位服务已广泛应用在室外场景。Outdoor positioning and location-based services have matured. Location services based on global satellite navigation and positioning systems and maps are widely used. The principle is to use the distance between the user terminal and known satellites to measure the user's accurate location information, which can well meet the positioning needs in outdoor environments. For outdoor scenes, the United States' Global Positioning System (GPS), Russia's GLONASS, the European Union's Galileo and China's Beidou Satellite Navigation System (BDS) have covered the world, and related navigation and positioning services have been widely used in outdoor scenes.
但是随着社会现代化的推进，人们的主要活动越来越集中在室内。据统计，人们每天80％的时间都是在室内环境中度过，这一实际需求也促使了室内定位技术的研究。但通常室内环境复杂，卫星信号在室内环境下衰减严重，且卫星定位精度较差，无法满足室内定位的需求。除此之外，待测目标与探测器之间障碍物多，这使得传输信号表现为多径密集，非视距传播。因此传统室外定位系统，无法很好满足室内定位应用的需求。一系列基于室内定位的技术应运而生，如红外(Infrared)技术，无线局域网(WLAN)技术，蓝牙(Bluetooth)技术，超宽带(UWB)技术，射频识别(RFID)技术等，在室内场景中均有广泛应用。However, with the advancement of social modernization, people's main activities are increasingly concentrated indoors. According to statistics, people spend 80% of their time indoors every day. This practical demand has also prompted the research of indoor positioning technology. However, the indoor environment is usually complex, the satellite signal is severely attenuated in the indoor environment, and the satellite positioning accuracy is poor, which cannot meet the needs of indoor positioning. In addition, there are many obstacles between the target to be measured and the detector, which makes the transmission signal appear to be multipath dense and non-line-of-sight propagation. Therefore, the traditional outdoor positioning system cannot meet the needs of indoor positioning applications. A series of technologies based on indoor positioning have emerged, such as infrared technology, wireless local area network (WLAN) technology, Bluetooth technology, ultra-wideband (UWB) technology, radio frequency identification (RFID) technology, etc., which are widely used in indoor scenes.
但其实现在并没有一套完善的系统可以很好的满足室内定位的需求，现有的大多数定位系统都是针对静止目标的室内定位，通过对定位算法的改进优化，从而提高静止目标室内定位系统的定位精度。而针对移动目标的定位系统，则是侧重于提出优化不同的室内定位跟踪算法以及移动节点的路径规划等，由是否需要测距又可以将定位算法分为基于测距的定位算法和无需测距的定位算法。传统的基于测距的定位算法主要有三边测量法、三角测量法、最大似然估计法等。无需测距的定位算法主要有质心算法(CentroidLocalizationAlgorithm)、凸规划定位算法(Convex Programming LocalizationAlgorithm)、DV-Hop(DistanceVector-Hop)算法、Amorphous算法、MDS(MultidimensionalScaling)算法、APIT(Approximate Point-In-TriangulationText)算法等。But in fact, there is no perfect system that can well meet the needs of indoor positioning. Most of the existing positioning systems are for indoor positioning of stationary targets. By improving and optimizing the positioning algorithm, the positioning accuracy of the indoor positioning system of stationary targets can be improved. The positioning system for mobile targets focuses on proposing and optimizing different indoor positioning tracking algorithms and path planning of mobile nodes. The positioning algorithm can be divided into ranging-based positioning algorithms and positioning algorithms without ranging according to whether ranging is required. The traditional ranging-based positioning algorithms mainly include triangulation, triangulation, maximum likelihood estimation, etc. The positioning algorithms without ranging mainly include centroid algorithm (CentroidLocalizationAlgorithm), convex programming positioning algorithm (Convex Programming LocalizationAlgorithm), DV-Hop (DistanceVector-Hop) algorithm, Amorphous algorithm, MDS (MultidimensionalScaling) algorithm, APIT (Approximate Point-In-TriangulationText) algorithm, etc.
对于基于测距的室内定位系统而言，不论是静止目标室内定位系统还是移动目标室内定位系统，在理论分析上，任一时刻多个参考节点(Reference Node，RN)与某一目标节点(BlindNode，BN)之间的通信测距过程都是在该时刻同时进行的，即任一时刻可以进行多组参考节点与同一待测目标的通信测距。然而在室内定位系统的实际应用中，当采用时分复用方式实现多个参考节点与待测目标的测距过程时，由于信道、频率以及硬件模块等的限制，除广播通信外，在任一时刻各个参考节点与待测目标之间只能进行点对点通信，因此在任一时刻多个参考节点无法同时与待测目标进行通信过程，即各个参考节点需分时与待测目标进行测距，以避免通信冲突。对于静止目标的室内定位系统而言，参考节点的分时测距对其并无影响，然而对于移动目标的室内定位系统而言，由于参考节点的分时以及待测目标的移动性，在任一参考节点与待测目标进行测距过程中，待测目标也在不断运动，因此当下一参考节点再对待测目标进行测距时，待测目标的位置已经发生改变。因此在整个定位过程中，参考节点与待测目标进行多组分时测距时待测目标的移动则成为了引起定位误差的主要因素。For indoor positioning systems based on ranging, whether it is a stationary target indoor positioning system or a mobile target indoor positioning system, in theoretical analysis, the communication ranging process between multiple reference nodes (RN) and a target node (BlindNode, BN) at any time is carried out simultaneously at that time, that is, communication ranging between multiple groups of reference nodes and the same target to be measured can be carried out at any time. However, in the actual application of indoor positioning systems, when the ranging process between multiple reference nodes and the target to be measured is realized by time division multiplexing, due to the limitations of channels, frequencies, and hardware modules, except for broadcast communication, at any time, each reference node can only perform point-to-point communication with the target to be measured. Therefore, at any time, multiple reference nodes cannot simultaneously communicate with the target to be measured, that is, each reference node needs to perform ranging with the target to be measured in time division to avoid communication conflicts. For the indoor positioning system of stationary targets, the time-sharing ranging of the reference node has no effect on it. However, for the indoor positioning system of moving targets, due to the time-sharing of the reference node and the mobility of the target to be measured, the target to be measured is also constantly moving during the ranging process between any reference node and the target to be measured. Therefore, when the next reference node measures the distance between the target to be measured, the position of the target to be measured has changed. Therefore, in the entire positioning process, the movement of the target to be measured when the reference node and the target to be measured perform multi-group time-sharing ranging becomes the main factor causing positioning error.
基于此，亟需一种能够提高定位精度的定位方法及系统。Based on this, there is an urgent need for a positioning method and system that can improve positioning accuracy.
发明内容Summary of the invention
本发明的目的是提供一种室内移动目标定位方法及系统，对经典室内定位系统模型重新进行系统建模，使其更加适用于室内移动目标定位系统的实际应用，提高室内移动目标的定位精度。The purpose of the present invention is to provide a method and system for indoor mobile target positioning, to remodel the classic indoor positioning system model, to make it more suitable for practical application of indoor mobile target positioning system, and to improve the positioning accuracy of indoor mobile targets.
为实现上述目的，本发明提供了如下方案：To achieve the above object, the present invention provides the following solutions:
一种室内移动目标定位方法，所述定位方法包括：A method for positioning an indoor mobile target, the method comprising:
考虑多个参考节点的分时测距过程中待测目标的移动性，对经典室内定位系统模型重新进行系统建模，得到改进后室内定位系统模型；Considering the mobility of the target in the time-sharing ranging process of multiple reference nodes, the classic indoor positioning system model is remodeled to obtain an improved indoor positioning system model.
根据所述改进后室内定位系统模型对经典室内定位算法进行改进，得到改进后室内定位算法；The classic indoor positioning algorithm is improved according to the improved indoor positioning system model to obtain an improved indoor positioning algorithm;
利用所述改进后室内定位算法对所述改进后室内定位系统模型进行求解，得到所述待测目标的定位结果。The improved indoor positioning algorithm is used to solve the improved indoor positioning system model to obtain the positioning result of the target to be measured.
一种室内移动目标定位系统，所述定位系统包括：An indoor mobile target positioning system, the positioning system comprising:
模型改进模块，用于考虑多个参考节点的分时测距过程中待测目标的移动性，对经典室内定位系统模型重新进行系统建模，得到改进后室内定位系统模型；The model improvement module is used to consider the mobility of the target to be measured during the time-sharing ranging process of multiple reference nodes, remodel the classic indoor positioning system model, and obtain an improved indoor positioning system model;
算法改进模块，用于根据所述改进后室内定位系统模型对经典室内定位算法进行改进，得到改进后室内定位算法；An algorithm improvement module is used to improve the classic indoor positioning algorithm according to the improved indoor positioning system model to obtain an improved indoor positioning algorithm;
定位模块，用于利用所述改进后室内定位算法对所述改进后室内定位系统模型进行求解，得到所述待测目标的定位结果。The positioning module is used to solve the improved indoor positioning system model by using the improved indoor positioning algorithm to obtain the positioning result of the target to be measured.
根据本发明提供的具体实施例，本发明公开了以下技术效果：According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:
本发明用于提供一种室内移动目标定位方法及系统，先考虑多个参考节点的分时测距过程中待测目标的移动性，对经典室内定位系统模型重新进行系统建模，得到改进后室内定位系统模型。再根据改进后室内定位系统模型对经典室内定位算法进行改进，得到改进后室内定位算法。最后利用改进后室内定位算法对改进后室内定位系统模型进行求解，得到待测目标的定位结果。本发明针对室内移动目标定位系统，将任意时刻多个参考节点对待测目标的分时测距以及在进行多组分时测距过程中待测目标的移动性考虑在内，对经典室内定位系统模型重新进行系统建模以及算法研究，使其更加适用于室内移动目标定位系统，实现定位误差的有效抑制，提高系统定位性能。The present invention is used to provide an indoor mobile target positioning method and system. The mobility of the target to be measured in the time-sharing ranging process of multiple reference nodes is first considered, and the classic indoor positioning system model is remodeled to obtain an improved indoor positioning system model. Then, the classic indoor positioning algorithm is improved according to the improved indoor positioning system model to obtain an improved indoor positioning algorithm. Finally, the improved indoor positioning system model is solved using the improved indoor positioning algorithm to obtain the positioning result of the target to be measured. The present invention is aimed at an indoor mobile target positioning system, and the time-sharing ranging of the target to be measured by multiple reference nodes at any time and the mobility of the target to be measured in the multi-group time-sharing ranging process are taken into consideration. The classic indoor positioning system model is remodeled and the algorithm is studied to make it more suitable for indoor mobile target positioning systems, realize effective suppression of positioning errors, and improve system positioning performance.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
图1为本发明实施例1所提供的点对点通信模式下的测距时序图；FIG1 is a timing diagram of ranging in a point-to-point communication mode provided in Embodiment 1 of the present invention;
图2为本发明实施例1所提供的定位方法的方法流程图；FIG2 is a flow chart of a positioning method provided by Embodiment 1 of the present invention;
图3为本发明实施例1所提供的改进后室内定位系统模型的示意图；FIG3 is a schematic diagram of an improved indoor positioning system model provided by Example 1 of the present invention;
图4为本发明实施例1所提供的改进后taylor定位算法的流程图；FIG4 is a flow chart of the improved Taylor positioning algorithm provided by Example 1 of the present invention;
图5为本发明实施例1所提供的两种模型下taylor定位算法定位性能基于距离偏量的变化曲线图；FIG5 is a curve diagram showing the change of positioning performance of the Taylor positioning algorithm based on distance deviation under two models provided in Example 1 of the present invention;
图6为本发明实施例1所提供的两种模型下taylor定位算法定位性能基于运动速度的变化曲线图；FIG6 is a curve diagram showing the change of positioning performance of the Taylor positioning algorithm based on the motion speed under two models provided in Example 1 of the present invention;
图7为本发明实施例1所提供的两种模型下taylor定位算法定位性能基于测距间隔时间的变化曲线图；FIG7 is a curve diagram showing the change of positioning performance of the Taylor positioning algorithm based on the ranging interval time under two models provided in Example 1 of the present invention;
图8为本发明实施例1所提供的DW1000的芯片结构图；FIG8 is a chip structure diagram of DW1000 provided in Example 1 of the present invention;
图9为本发明实施例1所提供的SDS-TWR测距流程图；FIG9 is a flow chart of SDS-TWR ranging provided by Example 1 of the present invention;
图10为本发明实施例1所提供的基于速度时间的测距模型下taylor定位算法定位性能基于测距间隔时间的变化曲线图；10 is a curve diagram showing the change of positioning performance of the Taylor positioning algorithm based on the ranging interval time under the speed-time-based ranging model provided in Example 1 of the present invention;
图11为本发明实施例2所提供的定位系统的系统框图。FIG. 11 is a system block diagram of a positioning system provided in Embodiment 2 of the present invention.
具体实施方式DETAILED DESCRIPTION
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
本发明的目的是提供一种室内移动目标定位方法及系统，涉及物联网领域、无线传感器领域以及室内定位领域，对经典室内定位系统模型重新进行系统建模，使其更加适用于室内移动目标定位系统的实际应用，提高室内移动目标的定位精度。The purpose of the present invention is to provide an indoor mobile target positioning method and system, which relate to the fields of Internet of Things, wireless sensor and indoor positioning, and remodel the classic indoor positioning system model to make it more suitable for the practical application of indoor mobile target positioning system, thereby improving the positioning accuracy of indoor mobile targets.
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.
实施例1：Embodiment 1:
对于基于测距技术的室内定位系统而言，不论是静止目标室内定位系统还是移动目标室内定位系统，在经典室内定位系统的理论模型中，认为任意时刻某一待测目标可以与多个参考节点同时通信进行距离测量，然而在室内定位系统的实际应用中，当采用时分复用方式实现多个参考节点与待测目标的测距过程时，由于信道、频率以及硬件模块等的限制，除广播通信外，在任一时刻各个参考节点与待测目标之间只能进行点对点通信，即任一时刻待测目标并没有办法同时与多个参考节点进行通信测距。For indoor positioning systems based on ranging technology, whether it is a stationary target indoor positioning system or a mobile target indoor positioning system, in the theoretical model of the classic indoor positioning system, it is believed that a target to be measured can communicate with multiple reference nodes simultaneously to measure the distance at any time. However, in the actual application of the indoor positioning system, when time division multiplexing is used to realize the ranging process between multiple reference nodes and the target to be measured, due to the limitations of channels, frequencies and hardware modules, except for broadcast communication, at any time, each reference node and the target to be measured can only communicate point-to-point, that is, at any time, the target to be measured has no way to communicate and measure distance with multiple reference nodes at the same time.
如图1所示，其为m个参考节点(RN1-RNm)与待测目标节点BN在点对点通信模式下的测距时序过程(在二维坐标系下，至少需要三个参考节点才能实现对待测目标的定位，即m需大于等于3；在三维坐标系下，m≥4)。在t0时刻参考节点RN1与待测目标节点BN进行通信测距，在t1时刻RN1完成与BN的通信测距过程，开始进行参考节点RN2与BN的通信测距，在t2时刻完成。以此类推，在tm-1时刻开始进行参考节点RNm与BN的通信测距，在tm时刻完成。至此，所有参考节点才完成与待测目标节点的通信测距过程，测距周期为T，通过各个参考节点获取到与待测目标节点的测距信息，服务器可以对待测目标节点的位置进行定位。As shown in Figure 1, it is a ranging timing process between m reference nodes (RN1 -RNm ) and the target node BN to be measured in a point-to-point communication mode (in a two-dimensional coordinate system, at least three reference nodes are required to realize the positioning of the target to be measured, that is, m must be greater than or equal to 3; in a three-dimensional coordinate system, m≥4). At time t0 , the reference node RN1 performs communication ranging with the target node BN to be measured. At time t1 , RN1 completes the communication ranging process with BN, and starts the communication ranging between the reference node RN2 and BN, which is completed at time t2. Similarly, at time tm-1 , the communication ranging between the reference node RNm and BN is started and completed at time tm . At this point, all reference nodes have completed the communication ranging process with the target node to be measured. The ranging period is T. The ranging information with the target node to be measured is obtained through each reference node, and the server can locate the position of the target node to be measured.
由上述的时序分析可知，在基于时分复用方式的室内定位系统的实际应用中，在点对点通信模式下，任意时刻，多个参考节点RN1-RNm对待测目标的测距需分时进行，以避免通信冲突。在广播通信模式下，对于多目标定位系统，当任一待测目标通过广播与多个参考节点进行通信测距时，为避免通信冲突，其他待测目标只能等候，即在广播模式下，涉及的则是待测目标的分时测距，是本实施例研究问题的另一种情况。然而在经典室内定位系统模型的理论分析中，却认为任意时刻多个参考节点与某一待测目标之间的通信测距过程都是在该时刻同时进行的，即认为多个参考节点对待测目标的测距周期T为单位时间，任一时刻可以进行多组参考节点与待测目标的通信测距过程。在k时刻下，经典室内定位系统的测距模型(即本实施例所述的经典室内定位系统模型)如下：It can be seen from the above timing analysis that in the actual application of the indoor positioning system based on the time division multiplexing mode, in the point-to-point communication mode, at any time, the distance measurement of the target to be measured by multiple reference nodes RN1 -RNm needs to be carried out in time-sharing to avoid communication conflicts. In the broadcast communication mode, for the multi-target positioning system, when any target to be measured communicates and measures distance with multiple reference nodes through broadcasting, in order to avoid communication conflicts, other targets to be measured can only wait, that is, in the broadcast mode, what is involved is the time-sharing distance measurement of the target to be measured, which is another situation of the research problem of this embodiment. However, in the theoretical analysis of the classic indoor positioning system model, it is believed that the communication and ranging process between multiple reference nodes and a certain target to be measured at any time is carried out simultaneously at that moment, that is, it is believed that the ranging period T of multiple reference nodes to the target to be measured is a unit time, and the communication and ranging process of multiple groups of reference nodes and the target to be measured can be carried out at any time. At time k, the ranging model of the classic indoor positioning system (that is, the classic indoor positioning system model described in this embodiment) is as follows:
式(1)中，di,k表示在k时刻下，第i个参考节点与待测目标之间的距离；(xk，yk，zk)为在k时刻，待测目标的位置坐标；(xi，yi，zi)为在k时刻，第i个参考节点的位置坐标；vk表示测量噪声，且vk服从N(0，Rk)分布。In formula (1), di,k represents the distance between the i-th reference node and the target to be measured at time k; (xk , yk , zk ) is the position coordinate of the target to be measured at time k; (xi , yi , zi ) is the position coordinate of the i-th reference node at time k; vk represents the measurement noise, and vk obeys N (0, Rk ) distribution.
对于静止目标的室内定位系统而言，参考节点的分时测距对其并无影响。然而对于移动目标室内定位系统，由于参考节点的分时测距以及待测目标的移动性，在任一参考节点与目标进行测距的过程中，待测目标也在不断运动，因此当下一参考节点再对目标进行测距时，待测目标的位置已经发生改变，从而导致上述经典室内定位系统模型并不适用于移动目标定位系统的实际应用，其参考节点与待测目标进行多组分时测距时待测目标的移动性也成为了引起移动目标定位系统定位误差较大的关键因素。For the indoor positioning system of stationary targets, the time-sharing ranging of the reference node has no effect on it. However, for the indoor positioning system of mobile targets, due to the time-sharing ranging of the reference node and the mobility of the target to be measured, the target to be measured is also constantly moving during the ranging process between any reference node and the target. Therefore, when the next reference node measures the distance to the target again, the position of the target to be measured has changed, resulting in the above-mentioned classic indoor positioning system model not being applicable to the actual application of the mobile target positioning system. The mobility of the target to be measured when the reference node and the target to be measured perform multi-group time-sharing ranging also becomes a key factor causing large positioning errors in the mobile target positioning system.
因此，本实施例针对室内移动目标定位系统，将任意时刻多个参考节点对待测目标的分时测距以及在进行多组分时测距过程中待测目标的移动性考虑在内，对经典室内定位系统模型重新进行系统建模，从而使其更加适用于室内移动目标定位系统的实际应用，并且基于新建立的系统模型进行经典室内定位算法的理论推导，进而分析室内移动目标定位系统中主要系统参数对其定位性能的影响，从而为实际移动目标室内定位系统的应用提供理论基础以及参数指标。Therefore, this embodiment, for the indoor mobile target positioning system, takes into account the time-sharing ranging of the target to be measured by multiple reference nodes at any time and the mobility of the target to be measured during the multi-group time-sharing ranging process, and remodels the classic indoor positioning system model to make it more suitable for the practical application of the indoor mobile target positioning system. The theoretical derivation of the classic indoor positioning algorithm is carried out based on the newly established system model, and then the influence of the main system parameters in the indoor mobile target positioning system on its positioning performance is analyzed, thereby providing a theoretical basis and parameter indicators for the application of the actual mobile target indoor positioning system.
具体的，本实施例用于提供一种室内移动目标定位方法，如图2所示，所述定位方法包括：Specifically, this embodiment is used to provide an indoor mobile target positioning method, as shown in FIG2 , the positioning method includes:
S1：考虑多个参考节点的分时测距过程中待测目标的移动性，对经典室内定位系统模型重新进行系统建模，得到改进后室内定位系统模型；S1: Considering the mobility of the target to be measured in the time-sharing ranging process of multiple reference nodes, the classic indoor positioning system model is remodeled to obtain an improved indoor positioning system model;
具体的，本实施例建立了应用于三维坐标系下的m个固定参考节点对任一个待测移动目标的改进后室内定位系统模型，如图3所示，矩形代表移动目标的运动起始点，三角形代表移动目标的运动终止点，实线代表移动目标的实际运动轨迹，而虚线则代表m个参考节点在对待测目标进行分时测距过程中待测目标的运动轨迹，用以形象的表明将多个参考节点的分时测距以及待测目标的移动性考虑在内而建立的室内移动目标定位系统模型。本实施例根据移动目标室内定位系统中多个参考节点的分时测距以及在进行多组分时测距期间待测目标的移动性特性，对经典室内定位系统模型(如式(1)所示)重新进行系统建模，建立了基于距离偏量的测距模型和基于速度时间的测距模型。即本实施例所述的改进后室内定位系统模型包括基于距离偏量的测距模型和基于速度时间的测距模型。Specifically, this embodiment establishes an improved indoor positioning system model for any mobile target to be measured using m fixed reference nodes in a three-dimensional coordinate system. As shown in FIG3 , a rectangle represents the starting point of the mobile target, a triangle represents the end point of the mobile target, a solid line represents the actual motion trajectory of the mobile target, and a dotted line represents the motion trajectory of the target to be measured during the time-sharing ranging of the m reference nodes, which is used to vividly illustrate the indoor mobile target positioning system model established by taking into account the time-sharing ranging of multiple reference nodes and the mobility of the target to be measured. This embodiment remodels the classic indoor positioning system model (as shown in formula (1)) based on the time-sharing ranging of multiple reference nodes in the mobile target indoor positioning system and the mobility characteristics of the target to be measured during the multi-group time-sharing ranging, and establishes a ranging model based on distance deviation and a ranging model based on speed time. That is, the improved indoor positioning system model described in this embodiment includes a ranging model based on distance deviation and a ranging model based on speed time.
(1)基于距离偏量的测距模型(1) Distance measurement model based on distance deviation
由于多个参考节点的分时测距以及待测目标的移动性，在任一参考节点与待测目标进行通信测距的过程中，待测目标仍在不断运动，因此当下一参考节点对待测目标进行测距时，待测目标的位置已经发生改变，参考节点观测的目标位置也随着其运动而不断变化。定义第i个参考节点(RNi)的位置坐标为RNi＝(xi,yi,zi)，在任意时刻k，参考节点观测待测目标的第i个位置坐标为BNk(i)＝(xk(i),yk(i),zk(i))，则m个参考节点对待测目标的测距观测模型如下：Due to the time-sharing ranging of multiple reference nodes and the mobility of the target to be measured, the target to be measured is still moving during the communication and ranging process between any reference node and the target to be measured. Therefore, when the next reference node measures the distance of the target to be measured, the position of the target to be measured has changed, and the target position observed by the reference node also changes with its movement. Define the position coordinates of the i-th reference node (RNi ) as RNi = (xi , yi , zi ). At any time k, the i-th position coordinates of the target to be measured observed by the reference node are BNk(i) = (xk(i) , yk(i) , zk(i) ). Then the ranging observation model of the target to be measured by m reference nodes is as follows:
式(2)中，di,k表示在k时刻，第i个参考节点观测到的待测目标距离，即第i个参考节点与待测目标之间的距离，i＝1,2,…,j,…,m，m为参考节点的个数；vk表示测量噪声，vk服从N(0,Rk)分布。In formula (2), di,k represents the distance of the target to be measured observed by the ith reference node at time k, that is, the distance between the ith reference node and the target to be measured, i = 1, 2, …, j, …, m, m is the number of reference nodes; vk represents the measurement noise, and vk obeys the N(0, Rk ) distribution.
然而在实际的定位理论分析中，认为在任意时刻k，通过求解参考节点观测的与待测目标的多个距离方程组成的上述方程组(即式(2))，得出的待定位节点(即待测目标)在该时刻下的位置仅为单个点坐标(xk,yk,zk)，因此k时刻，在参考节点与待测目标进行多组测距过程中，将参考节点测得的第一个待测目标位置定义为待测目标的实际位置，也就是式(2)中的待求位置，即BNk(1)(xk(1),yk(1),zk(1))＝BN(xk,yk,zk)。那么在测距过程中由于待测目标的不断移动，后续参考节点观测到的待测目标位置的偏移即为在移动目标定位系统中引起定位误差的关键因素。However, in the actual positioning theory analysis, it is believed that at any time k, by solving the above equation group composed of multiple distance equations between the reference node and the target to be measured (i.e., equation (2)), the position of the node to be located (i.e., the target to be measured) at that time is only a single point coordinate (xk , yk , zk ). Therefore, at time k, in the process of multiple sets of distance measurement between the reference node and the target to be measured, the first position of the target to be measured measured by the reference node is defined as the actual position of the target to be measured, that is, the position to be calculated in equation (2), that is, BNk(1) (xk(1) , yk(1) , zk(1) ) = BN (xk , yk , zk ). Then, due to the continuous movement of the target to be measured during the ranging process, the offset of the target position observed by the subsequent reference node is the key factor causing positioning error in the mobile target positioning system.
定义后续任意两个参考节点观测的待测目标位置之间的距离偏移量均相等，为δd，其中δd＝(δx,δy,δz)，则后续参考节点测得的待测目标位置均可由第一个参考节点测得的待测目标位置推导，即k时刻，参考节点观测的第i个待测目标位置为BNk(i)＝BNk(i-1)+δd，则m个参考节点对移动目标的观测模型(也即本实施例所述的基于距离偏量的测距模型)变成：Define that the distance offset between the positions of the target to be measured observed by any two subsequent reference nodes is equal, which is δd , where δd =(δx ,δy ,δz ). Then the positions of the target to be measured measured by the subsequent reference nodes can be derived from the position of the target to be measured measured by the first reference node. That is, at time k, the i-th position of the target to be measured observed by the reference node is BNk(i) =BNk(i-1) +δd . Then the observation model of the mobile target by the m reference nodes (that is, the distance measurement model based on the distance offset described in this embodiment) becomes:
式(3)中，di,k为在k时刻，第i个参考节点与待测目标之间的距离，i＝1,2,…,j,…,m，m为参考节点的个数；(xk，yk，zk)为在k时刻，待测目标的位置坐标；(δx，δy，δz)为在k时刻，两个参考节点测得的待测目标位置坐标之间的距离偏移量；(xi，yi，zi)为在k时刻，第i个参考节点的位置坐标；vk为测量噪声。In formula (3), di,k is the distance between the i-th reference node and the target to be measured at time k, i = 1, 2, …, j, …, m, m is the number of reference nodes; (xk , yk , zk ) is the position coordinate of the target to be measured at time k; (δx , δy , δz ) is the distance offset between the position coordinates of the target to be measured measured by the two reference nodes at time k; (xi , yi , zi ) is the position coordinate of the i-th reference node at time k; vk is the measurement noise.
(2)基于速度时间的测距模型(2) Speed-time-based distance measurement model
任意两个参考节点观测的待测目标位置之间的距离偏移量δd还可以表示为待测目标运动速度与任意两个参考节点间的测距间隔时间的乘积，在基于距离偏量的测距模型中定义任意待测目标位置之间的距离偏移量δd均相等，则在基于速度时间的测距模型中则定义待测目标一直保持匀速运动，速度为v，且任意两个参考节点间的测距间隔时间相等，均为δt，则δd＝(vx×δt,vy×δt,vz×δt)，建立待测目标运动模型如下：The distance offset δd between the positions of the target to be measured observed by any two reference nodes can also be expressed as the product of the moving speed of the target to be measured and the ranging interval time between any two reference nodes. In the ranging model based on distance offset, the distance offset δd between any positions of the target to be measured is defined to be equal. Then, in the ranging model based on speed time, the target to be measured is defined to keep moving at a uniform speed, with a speed of v, and the ranging interval time between any two reference nodes is equal, which is δt . Then δd = (vx ×δt ,vy ×δt ,vz ×δt ), and the motion model of the target to be measured is established as follows:
式(4)中，(Px,k,Py,k,Pz,k)表示待测目标在k时刻的位置；(Vx,k,Vy,k,Vz,k)表示待测目标在k时刻的速度；T表示单位采样间隔；ωk表示过程噪声，ωk服从N(0,Qk)分布。In formula (4), (Px,k , Py,k , Pz,k ) represents the position of the target at time k; (Vx,k ,Vy,k ,Vz,k ) represents the velocity of the target at time k; T represents the unit sampling interval;ωk represents the process noise, andωk obeys N(0,Qk ) distribution.
由以上研究可知，在任意时刻k，参考节点观测到的第一个目标节点位置即为待测目标的真实位置，即Zk,1＝Sk，则其他参考节点观测的待测目标位置模型为：From the above research, we can know that at any time k, the first target node position observed by the reference node is the true position of the target to be measured, that is, Zk,1 = Sk , then the position model of the target to be measured observed by other reference nodes is:
式(5)中，由于本实施例建立的系统模型中共有m个固定参考节点，且参考节点观测到的第一个移动目标位置已经给出，因此在这里i为正整数，且2≤i≤m；Zk,i为k时刻第i个参考节点观测到的待测目标的位置。In formula (5), since there are m fixed reference nodes in the system model established in this embodiment, and the first moving target position observed by the reference node has been given, i is a positive integer and 2≤i≤m; Zk,i is the position of the target to be measured observed by the i-th reference node at time k.
由上述的待测目标运动模型和参考节点观测到的待测目标位置模型，可以建立待测目标的观测模型(即本实施例所述的基于速度时间的测距模型)为：Based on the above-mentioned target motion model and the target position model observed by the reference node, the observation model of the target (i.e., the speed-time-based ranging model described in this embodiment) can be established as follows:
式(6)中，di,k为在k时刻，第i个参考节点与待测目标之间的距离，i＝1,2,…,j,…,m，m为参考节点的个数；(Px,k，Py,k，Pz,k)为在k时刻，待测目标的位置坐标；δt为两个参考节点间的测距间隔时间；(Vx,k，Vy,k，Vz,k)为在k时刻，待测目标的速度；(xi，yi，zi)为在k时刻，第i个参考节点的位置坐标；vk为测量噪声。In formula (6), di,k is the distance between the i-th reference node and the target to be measured at time k, i = 1, 2, …, j, …, m, m is the number of reference nodes; (Px,k , Py,k , Pz,k ) is the position coordinate of the target to be measured at time k; δt is the ranging interval time between two reference nodes; (Vx,k , Vy,k , Vz,k ) is the velocity of the target to be measured at time k; (xi ,yi , zi ) is the position coordinate of the i-th reference node at time k;vk is the measurement noise.
S2：根据所述改进后室内定位系统模型对经典室内定位算法进行改进，得到改进后室内定位算法；S2: improving the classic indoor positioning algorithm according to the improved indoor positioning system model to obtain an improved indoor positioning algorithm;
由于经典室内定位算法是基于经典室内定位系统模型(即式(1))而求解出待测目标的位置，而通过上述分析，可以知道经典室内定位系统模型并不适用于本实施例所述的室内移动目标的定位场景，因此S1针对室内移动目标，对经典室内定位系统模型进行了系统的重新建模，建立了两种测距模型，因测距模型的改进，经典室内定位算法已不适用，因此，本实施例对经典室内定位算法进行改进，使其更加适用于移动目标定位系统的实际应用，并且能大大减小由参考节点的分时测距以及待测目标的移动而引起的定位误差。总而言之，本实施例的定位算法是基于测距模型而进行推导研究的，因测距模型的改进，定位算法也进行了相应的改进。Since the classic indoor positioning algorithm is based on the classic indoor positioning system model (i.e., formula (1)) to solve the position of the target to be measured, and through the above analysis, it can be known that the classic indoor positioning system model is not suitable for the positioning scenario of the indoor mobile target described in this embodiment, S1 systematically remodels the classic indoor positioning system model for indoor mobile targets and establishes two ranging models. Due to the improvement of the ranging model, the classic indoor positioning algorithm is no longer applicable. Therefore, this embodiment improves the classic indoor positioning algorithm to make it more suitable for the practical application of the mobile target positioning system, and can greatly reduce the positioning error caused by the time-sharing ranging of the reference node and the movement of the target to be measured. In short, the positioning algorithm of this embodiment is derived and studied based on the ranging model, and due to the improvement of the ranging model, the positioning algorithm is also improved accordingly.
S3：利用所述改进后室内定位算法对所述改进后室内定位系统模型进行求解，得到所述待测目标的定位结果。S3: using the improved indoor positioning algorithm to solve the improved indoor positioning system model to obtain the positioning result of the target to be measured.
本实施例的经典室内定位算法可为taylor定位算法，基于S1新建立的改进后室内定位系统模型，进行了一种室内经典定位算法的理论研究，对taylor定位算法进行重新推导，改进后室内定位算法为改进后的taylor定位算法，则S3可以包括：The classic indoor positioning algorithm of this embodiment may be the Taylor positioning algorithm. Based on the improved indoor positioning system model newly established in S1, a theoretical study of a classic indoor positioning algorithm is conducted, and the Taylor positioning algorithm is re-derived. The improved indoor positioning algorithm is the improved Taylor positioning algorithm. Then S3 may include:
(1)对待测目标的位置进行初始化，得到初始估计位置；(1) Initialize the position of the target to be measured to obtain an initial estimated position;
(2)用taylor级数将改进后室内定位系统模型中的距离表达式在初始估计位置处展开，并忽略二次以上展开项，得到测量值误差矢量；(2) Using Taylor series, the distance expression in the improved indoor positioning system model is expanded at the initial estimated position, and the expansion terms above the second order are ignored to obtain the measurement error vector;
(3)令测量值误差矢量为0，利用加权最小二乘法计算得到误差；(3) Let the measurement error vector be 0 and use the weighted least squares method to calculate the error;
(4)判断误差是否小于预设门限值；(4) Determine whether the error is less than a preset threshold value;
(5)若是，则以初始估计位置作为待测目标的定位结果；(5) If yes, the initial estimated position is used as the positioning result of the target to be measured;
(6)若否，则以初始估计位置和误差的和作为下一循环中的初始估计位置，返回“用taylor级数将改进后室内定位系统模型中的距离表达式在初始估计位置处展开”的步骤。(6) If not, the sum of the initial estimated position and the error is used as the initial estimated position in the next loop, and the process returns to the step of "expanding the distance expression in the improved indoor positioning system model at the initial estimated position using the Taylor series".
具体的，本实施例以基于速度时间的测距模型为例，对taylor定位算法的改进过程进行进一步的说明：基于式(6)的测距模型得到距离表达式，将待测目标的运动速度v以及测距间隔时间等引入taylor定位算法中，得到改进后的taylor定位算法，使其更加适用于移动目标定位系统的实际应用，并且能大大减小由参考节点的分时测距以及待测目标的移动而引起的定位误差。Specifically, this embodiment takes the speed-time based ranging model as an example to further illustrate the improvement process of the Taylor positioning algorithm: based on the ranging model of formula (6), the distance expression is obtained, and the moving speed v of the target to be measured and the ranging interval time are introduced into the Taylor positioning algorithm to obtain the improved Taylor positioning algorithm, which makes it more suitable for the practical application of the mobile target positioning system and can greatly reduce the positioning error caused by the time-sharing ranging of the reference node and the movement of the target to be measured.
taylor定位算法(taylor级数展开算法)是一种需要初始估计位置的递归算法，每一次递归中，通过求解测距误差的局部最小二乘(LS)解来改进对标签的位置估计。如图4所示，其为改进后的taylor定位算法的流程图，包括如下步骤：The Taylor positioning algorithm (Taylor series expansion algorithm) is a recursive algorithm that requires an initial estimated position. In each recursion, the position estimate of the tag is improved by solving the local least squares (LS) solution of the ranging error. As shown in Figure 4, it is a flow chart of the improved Taylor positioning algorithm, which includes the following steps:
①假设待测目标的真实位置为(x,y,z)，初始估计位置为与真实值误差是δx，δy，δz，那么有：① Assume that the actual position of the target to be measured is (x, y, z), and the initial estimated position is The error from the true value is δx , δy , δz , then:
②由上述基于速度时间的系统建模，可得到距离表达式，距离表达式如下：② Based on the above speed-time based system modeling, the distance expression can be obtained, which is as follows:
将上述距离表达式在处用taylor级数展开，忽略二次以上的展开项则有：The above distance expression is Using Taylor series expansion, ignoring the expansion terms above the second order, we have:
式(8)中，di为参考节点到目标节点(即待测目标)的真实距离值。In formula (8), di is the actual distance value from the reference node to the target node (i.e., the target to be measured).
各项偏导项值为：The values of the partial derivatives are:
则有：Then we have:
令make
则可以得到测量值误差矢量为：Then the measurement error vector can be obtained as:
ψt＝ht-Gtδ； (12)ψt = ht -Gt δ; (12)
式(12)中，ψt为测量值误差矢量；ht为距离偏差矩阵；Gt为偏导矩阵；δ为误差。In formula (12),ψt is the measurement error vector;ht is the distance deviation matrix;Gt is the partial derivative matrix; δ is the error.
③令误差函数(即测量值误差矢量)ψt＝0，根据加权最小二乘法求得：③ Let the error function (i.e., the measurement error vector) ψt = 0, and obtain it according to the weighted least squares method:
δ＝(GtTQ-1Gt)-1GtTQ-1ht； (13)δ=(GtT Q-1 Gt )-1 GtT Q-1 ht ; (13)
式(13)中，Q是测量误差的协方差矩阵。In formula (13), Q is the covariance matrix of measurement error.
④判断是否小于给定门限值，若小于门限值，则是目标节点的定位结果；如果δ大于门限值，则继续进行第⑤步。④Judgment Is it less than the given threshold? If it is less than the threshold, then is the positioning result of the target node; if δ is greater than the threshold, proceed to step ⑤.
⑤令转到第②步继续执行。⑤ Order Go to step ② to continue.
进一步的，本实施例基于上述步骤中建立的系统测距模型以及推导的理论算法，对整个移动目标定位系统进行仿真研究，验证上述系统模型下定位算法的有效性，并且分析移动目标室内定位系统中主要系统参数对待测目标定位性能的影响。具体的，针对新建立的测距模型和定位算法对整个移动目标室内定位方案进行仿真分析，通过具体的数据分析，验证了本实施例重新推导的taylor定位算法的正确性、适用性以及有效性，可以有效抑制由多个参考节点对待测目标的分时测距以及在进行多组测距过程中待测目标的移动性而产生的定位误差。Furthermore, based on the system ranging model established in the above steps and the derived theoretical algorithm, this embodiment conducts a simulation study on the entire mobile target positioning system, verifies the effectiveness of the positioning algorithm under the above system model, and analyzes the influence of the main system parameters in the mobile target indoor positioning system on the positioning performance of the target to be measured. Specifically, the entire mobile target indoor positioning solution is simulated and analyzed for the newly established ranging model and positioning algorithm. Through specific data analysis, the correctness, applicability and effectiveness of the Taylor positioning algorithm re-derived in this embodiment are verified, which can effectively suppress the positioning error caused by the time-sharing ranging of the target to be measured by multiple reference nodes and the mobility of the target to be measured during the multi-group ranging process.
通过对经典室内定位系统模型和基于移动目标室内定位系统模型(即本实施例建立的两种改进后室内定位系统模型)的定位算法进行仿真分析，以均方根误差(RootMeanSquare Error，RMSE)，即位置真实值与估计值差值平方的均值的算数平方根作为判定标准，分析针对移动目标，在经典室内定位系统模型和基于本实施例所提的改进后室内定位系统模型下taylor定位算法的性能表现，以及几个系统参数对该算法定位性能的影响，为后续实际移动目标定位系统的应用提供理论基础和参数指标。Through simulation analysis of the positioning algorithm of the classic indoor positioning system model and the indoor positioning system model based on the mobile target (i.e., the two improved indoor positioning system models established in this embodiment), the root mean square error (RMSE), that is, the arithmetic square root of the mean of the square of the difference between the true value and the estimated value of the position, is used as the judgment criterion to analyze the performance of the Taylor positioning algorithm for mobile targets under the classic indoor positioning system model and the improved indoor positioning system model proposed in this embodiment, as well as the influence of several system parameters on the positioning performance of the algorithm, to provide a theoretical basis and parameter indicators for the subsequent application of the actual mobile target positioning system.
在三维坐标系下，均方根误差(RMSE)的公式如下：In a three-dimensional coordinate system, the formula for the root mean square error (RMSE) is as follows:
式(14)中，(x,y,z)为待测目标的真实位置；为由定位算法得出待测目标的估计位置。In formula (14), (x, y, z) is the actual position of the target to be measured; The estimated position of the target to be measured is obtained by the positioning algorithm.
在三维坐标系下，参考节点的位置固定不变，在本实施例的仿真实验中设置参考节点的位置坐标分别为RN1＝(20,0,0)，RN2＝(0,20,0)，RN3＝(0,0,20)，RN4＝(20,20,20)。任一待测目标在四个参考节点的覆盖范围内随机移动，待测目标的运动起始点为BN1(5,5,1.5)，待测目标以vx＝1m/s,vy＝1m/s,vz＝1m/s做匀速运动，单位采样间隔为T＝0.1s，采样次数为N＝120，过程噪声wk的协方差矩阵Qk如下式左，测量噪声vk的协方差矩阵Rk如下式右。In the three-dimensional coordinate system, the position of the reference node is fixed. In the simulation experiment of this embodiment, the position coordinates of the reference nodes are set to RN1 = (20, 0, 0), RN2 = (0, 20, 0), RN3 = (0, 0, 20), and RN4 = (20, 20, 20). Any target to be measured moves randomly within the coverage of the four reference nodes. The starting point of the movement of the target to be measured is BN1 (5, 5, 1.5). The target to be measured moves at a uniform speed of vx = 1 m/s, vy = 1 m/s, and vz = 1 m/s. The unit sampling interval is T = 0.1 s, the number of sampling times is N = 120, the covariance matrix Qk of the process noise wk is as follows on the left, and the covariance matrix Rk of the measurement noise vk is as follows on the right.
(1)基于距离偏量的测距模型的仿真分析(1) Simulation analysis of the distance measurement model based on distance deviation
针对室内移动目标定位系统，分别仿真了a.经典室内定位系统测距模型和b.基于距离偏量的测距模型下的taylor定位算法，并分析了两种不同系统模型下定位算法在基于距离偏量的模|δd|变化的定位性能表现。S1中定义任意两个参考节点观测到的待测目标位置之间的距离偏移量为δd，且δd＝(δx,δy,δz)，则For the indoor mobile target positioning system, the Taylor positioning algorithm under a classic indoor positioning system ranging model and a distance deviation based ranging model are simulated respectively, and the positioning performance of the positioning algorithm under the two different system models under the change of the distance deviation modulus |δd | is analyzed. In S1, the distance offset between the positions of the target to be measured observed by any two reference nodes is defined as δd , and δd = (δx ,δy ,δz ), then
如图5所示，其为在经典室内定位系统测距模型和基于距离偏量的测距模型下taylor定位算法随着|δd|变化的定位性能变化曲线图，变化曲线图的自变量为任意两个参考节点观测的待测目标位置之间的距离偏移量|δd|，变化范围为0-2(m)，变化曲线图的因变量为均方根误差RMSE值，用以评估定位系统定位性能的好坏，均方根误差的值越小，定位精度越高，定位性能的表现越好。图5中的部分仿真数据如下表1所示，表1随机选取了30组数据，表中Deltad代表任意两个参考节点观测的待测目标位置之间的距离偏移量|δd|，RMSE1代表经典taylor算法的均方根误差值，RMSE2代表基于距离偏量的测距模型下的taylor算法的均方根误差值。As shown in FIG5, it is a curve diagram of the positioning performance change of the Taylor positioning algorithm with the change of |δd | under the classic indoor positioning system ranging model and the ranging model based on the distance deviation. The independent variable of the variation curve diagram is the distance offset |δd | between the positions of the target to be measured observed by any two reference nodes, and the variation range is 0-2 (m). The dependent variable of the variation curve diagram is the root mean square error RMSE value, which is used to evaluate the positioning performance of the positioning system. The smaller the value of the root mean square error, the higher the positioning accuracy and the better the positioning performance. Some simulation data in FIG5 are shown in Table 1 below. Table 1 randomly selects 30 groups of data. In the table, Deltad represents the distance offset |δd | between the positions of the target to be measured observed by any two reference nodes, RMSE1 represents the root mean square error value of the classic Taylor algorithm, and RMSE2 represents the root mean square error value of the Taylor algorithm under the ranging model based on the distance deviation.
表1Table 1
(2)基于速度时间的测距模型的仿真分析(2) Simulation analysis of the speed-time-based distance measurement model
在基于速度时间的测距模型中，可以用待测目标的运动速度v与任意两个参考节点间的测距间隔时间δt的乘积，来表示基于距离偏量的测距模型中任意两个参考节点观测的待测目标位置之间的距离偏移量δd，即δd＝v×δt。因此，在这一部分，可将δd分解为两部分，分别探究待测目标的运动速度v和测距间隔时间δt这两个系统参数对定位性能的影响。In the speed-time based ranging model, the distance offset δd between the positions of the target to be measured observed by any two reference nodes in the distance deviation based ranging model can be expressed by the product of the moving speed v of the target to be measured and the ranging interval time δ tbetween any two reference nodes, that is, δd = v × δt . Therefore, in this part, δd can be decomposed into two parts, respectively exploring the influence of the two system parameters of the moving speed v of the target to be measured and the ranging interval time δt on the positioning performance.
①待测目标的运动速度v① The moving speed v of the target to be measured
针对室内移动目标定位系统，分别仿真了a.经典室内定位系统测距模型和b.基于速度时间的测距模型下的taylor定位算法，并分析了上述两种不同系统模型下定位算法在基于待测目标运动速度v变化的性能表现。For the indoor mobile target positioning system, the Taylor positioning algorithm under a. classic indoor positioning system ranging model and b. speed-time based ranging model are simulated respectively, and the performance of the positioning algorithm under the above two different system models based on the change of the moving speed v of the target to be measured is analyzed.
如图6所示，其为在经典室内定位系统模型和基于速度时间的测距模型下taylor定位算法随着待测目标的运动速度v变化的定位性能变化曲线图，变化曲线图的自变量为待测目标的运动速度变化范围为1-10(m/s)，变化曲线图的因变量为均方根误差RMSE值，用以评估定位系统定位性能的好坏，设定任意两个参考节点间的测距间隔时间为δt＝0.05s。图6中的部分仿真数据如下表2所示，表2是随机选取了30组数据，表中v代表待测目标的运动速度，RMSE1代表经典taylor算法的均方根误差值，RMSE2代表基于速度时间的测距模型下的taylor算法的均方根误差值。As shown in Figure 6, it is a curve diagram of the positioning performance change of the Taylor positioning algorithm as the moving speed v of the target changes under the classic indoor positioning system model and the speed-time based ranging model. The independent variable of the changing curve diagram is the moving speed of the target. The range of variation is 1-10 (m/s), and the dependent variable of the variation curve is the root mean square error RMSE value, which is used to evaluate the positioning performance of the positioning system. The ranging interval between any two reference nodes is set to δt = 0.05s. Some simulation data in Figure 6 are shown in Table 2 below. Table 2 randomly selects 30 sets of data. In the table, v represents the moving speed of the target to be measured, RMSE1 represents the root mean square error value of the classic Taylor algorithm, and RMSE2 represents the root mean square error value of the Taylor algorithm under the ranging model based on speed time.
表2Table 2
②任意两个参考节点间的测距间隔时间δt② The ranging interval time δt between any two reference nodes
在这一部分，针对室内移动目标定位系统，分别仿真了a.经典室内定位系统测距模型和b.基于速度时间的测距模型下的taylor定位算法，并分析了上述两种不同系统模型下定位算法在基于任意两个参考节点间的测距间隔时间δt变化的性能表现。In this part, for the indoor mobile target positioning system, the Taylor positioning algorithm is simulated under a. classic indoor positioning system ranging model and b. speed-time based ranging model respectively, and the performance of the positioning algorithm under the above two different system models based on the change of the ranging interval timeδt between any two reference nodes is analyzed.
如图7所示，其为在经典室内定位测距模型和基于速度时间的测距模型下taylor定位算法随着δt变化的定位性能变化曲线图，变化曲线图的自变量为任意两个参考节点间的测距间隔时间δt，变化范围为0-1(s)，变化曲线图的因变量为均方根误差RMSE值，用以评估定位系统定位性能的好坏。图7中的部分仿真数据如下表3所示，表3随机选取了30组数据，表中Deltat代表任意两个参考节点间的测距间隔时间，RMSE1代表经典taylor算法的均方根误差值，RMSE2代表基于速度时间的测距模型下的taylor算法的均方根误差值。As shown in Figure 7, it is a curve diagram of the positioning performance change of the Taylor positioning algorithm with the change of δt under the classic indoor positioning ranging model and the ranging model based on speed time. The independent variable of the variation curve diagram is the ranging interval time δt between any two reference nodes, and the variation range is 0-1 (s). The dependent variable of the variation curve diagram is the root mean square error RMSE value, which is used to evaluate the positioning performance of the positioning system. Some simulation data in Figure 7 are shown in Table 3 below. Table 3 randomly selects 30 groups of data. In the table, Deltat represents the ranging interval time between any two reference nodes, RMSE1 represents the root mean square error value of the classic Taylor algorithm, and RMSE2 represents the root mean square error value of the Taylor algorithm under the ranging model based on speed time.
表3Table 3
从以上三个基于不同系统参数而变化的定位性能曲线图可以看出，随着几种不同系统参数(距离偏移量|δd|、待测目标运动速度v、测距间隔时间δt)的不断变化，几种系统模型下的taylor定位算法的均方根误差值RMSE均随着自变量的增大而增大。此外，不论是基于距离偏量的测距模型还是基于速度时间的测距模型下的taylor定位算法的定位性能均明显优于经典室内定位系统测距模型下的算法，说明本实施例所提出的基于移动目标室内定位系统测距模型下的定位算法可以有效抑制由多个参考节点对待测目标的分时测距以及在进行多组测距过程中待测目标的移动性而产生的定位误差。It can be seen from the above three positioning performance curves based on different system parameters that with the continuous changes of several different system parameters (distance offset |δd |, target motion speed v, ranging interval time δt ), the root mean square error RMSE of the Taylor positioning algorithm under several system models increases with the increase of the independent variable. In addition, the positioning performance of the Taylor positioning algorithm under the ranging model based on the distance offset or the ranging model based on the speed time is obviously better than the algorithm under the ranging model of the classic indoor positioning system, indicating that the positioning algorithm under the ranging model of the mobile target indoor positioning system proposed in this embodiment can effectively suppress the positioning error caused by the time-sharing ranging of the target to be measured by multiple reference nodes and the mobility of the target to be measured in the process of performing multiple groups of ranging.
综上所述，通过建立更加符合实际情况下的室内移动目标定位系统模型，从而将多个参考节点对待测目标的分时测距以及在进行多组测距过程中待测目标的移动性引入其中，重新进行系统建模和算法理论推导，通过仿真分析验证了重新建模的系统模型以及定位算法的有效性，可以更好的抑制由于移动目标定位系统实际应用过程中的复杂性而带来对其定位性能的影响。In summary, by establishing an indoor mobile target positioning system model that is more in line with the actual situation, the time-sharing ranging of the target to be measured by multiple reference nodes and the mobility of the target to be measured during multiple groups of ranging are introduced into it, and the system modeling and algorithm theory are re-derived. The effectiveness of the re-modeled system model and positioning algorithm is verified through simulation analysis, which can better suppress the influence of the complexity of the actual application of the mobile target positioning system on its positioning performance.
根据上述研究分析，还可探究在满足不同室内定位系统性能要求的前提下，系统的参数设置问题等，为实际的移动目标室内定位系统的搭建部署提供理论基础以及参数指标。具体的，根据几种系统参数如：距离偏量|δd|、待测目标的运动速度v、参考节点的测距间隔时间δt等对定位性能的影响曲线图，在满足不同室内定位系统性能要求的前提下，探究对于不同运动速度的移动目标，其任意两个参考节点间的测距间隔时间的参数设置问题，使待测目标在这段间隔时间内，可以移动满足系统定位性能的距离偏移量，从而使得对于移动目标定位系统的研究更具实际意义，可以为实际的定位系统搭建过程以及参考节点的轮询时间等提供理论基础和参数指标。According to the above research and analysis, we can also explore the system parameter setting issues under the premise of meeting the performance requirements of different indoor positioning systems, and provide a theoretical basis and parameter indicators for the construction and deployment of the actual mobile target indoor positioning system. Specifically, according to the influence curves of several system parameters such as distance offset |δd |, the movement speed v of the target to be measured, and the ranging interval time δt of the reference node on the positioning performance, under the premise of meeting the performance requirements of different indoor positioning systems, we explore the parameter setting issues of the ranging interval time between any two reference nodes for mobile targets with different movement speeds, so that the target to be measured can move within this interval. The distance offset that meets the system positioning performance, so that the research on the mobile target positioning system is more practical, and can provide a theoretical basis and parameter indicators for the actual positioning system construction process and the polling time of the reference node.
本实施例针对采用时分复用方式下的室内移动目标定位系统，将任一时刻在基于点对点通信模式下多个参考节点对待测目标的分时测距以及在进行多组分时测距期间待测目标的移动性考虑在内，对经典室内定位系统模型进行了系统的重新建模，从而使其更加适用于移动目标定位系统的实际应用。并且基于新建立的系统模型进行了经典室内定位算法的理论推导以及仿真研究，进而分析了室内移动目标定位系统中主要系统参数对其定位性能的影响，从而为实际移动目标室内定位系统的应用提供理论基础以及参数指标。本实施例对经典室内定位系统进行了重新建模，建立了基于距离偏量的测距模型和基于速度时间的测距模型。基于距离偏量的测距模型将多个参考节点进行分时测距期间由于待测目标移动而产生的距离偏量δd引入测距模型；而基于速度时间的测距模型是将距离偏量δd分解为待测目标的运动速度v和测距间隔时间δt两部分，将运动速度v和测距间隔时间δt引入测距模型，基于上述测距模型，对经典的taylor定位算法进行了理论的重新推导以及仿真分析，相较于经典室内定位系统模型下的taylor定位算法，验证了基于上述系统模型下定位算法的有效性。此外，根据主要系统参数对定位性能的影响，为实际室内移动目标定位系统的搭建部署提供理论基础以及参数指标。This embodiment is aimed at an indoor mobile target positioning system using a time division multiplexing method. It takes into account the time-division ranging of the target to be measured by multiple reference nodes in a point-to-point communication mode at any time, as well as the mobility of the target to be measured during multi-group time-division ranging, and systematically remodels the classic indoor positioning system model, thereby making it more suitable for the practical application of the mobile target positioning system. In addition, based on the newly established system model, the theoretical derivation and simulation research of the classic indoor positioning algorithm are carried out, and then the influence of the main system parameters in the indoor mobile target positioning system on its positioning performance is analyzed, thereby providing a theoretical basis and parameter indicators for the application of the actual mobile target indoor positioning system. This embodiment remodels the classic indoor positioning system and establishes a ranging model based on distance deviation and a ranging model based on speed time. The distance deviation-based ranging model introduces the distance deviation δd generated by the movement of the target to be measured during the time-sharing ranging of multiple reference nodes into the ranging model; while the speed-time-based ranging model decomposes the distance deviation δd into two parts: the moving speed v of the target to be measured and the ranging interval time δt , and introduces the moving speed v and the ranging interval time δt into the ranging model. Based on the above ranging model, the classic Taylor positioning algorithm is theoretically re-derived and simulated. Compared with the Taylor positioning algorithm under the classic indoor positioning system model, the effectiveness of the positioning algorithm based on the above system model is verified. In addition, according to the influence of the main system parameters on the positioning performance, a theoretical basis and parameter indicators are provided for the construction and deployment of the actual indoor mobile target positioning system.
本实施例所建立的适用于室内移动目标定位系统的测距模型可以应用于各种室内定位技术以及系统中，超宽带(Ultra-wideband，UWB)技术是近年来发展起来的无线电技术，它不需要使用传统通信体制中的载波，而是使用纳秒至皮秒级的非正弦波窄脉冲传输数据，信号具有GHz量级的带宽。超宽带室内定位利用脉冲波的良好抗多径效应和精确测距特性，可以实现具有高精度、高可靠性的室内定位系统。因此在此简单介绍本实施例中的测距模型在UWB室内定位系统中的应用：The ranging model for indoor mobile target positioning system established in this embodiment can be applied to various indoor positioning technologies and systems. Ultra-wideband (UWB) technology is a radio technology developed in recent years. It does not need to use the carrier in the traditional communication system, but uses nanosecond to picosecond non-sinusoidal narrow pulses to transmit data, and the signal has a bandwidth of GHz. Ultra-wideband indoor positioning utilizes the good anti-multipath effect and precise ranging characteristics of pulse waves to achieve an indoor positioning system with high precision and high reliability. Therefore, the application of the ranging model in this embodiment in the UWB indoor positioning system is briefly introduced here:
超宽带技术是一种无载波的无线通信技术，具有传输速率快、测距精度高、功耗低、抗多径干扰能力强等特点。20世纪60年代，UWB最初用于军事目的，直到2002年美国联邦通信委员会(FCC)才发布商业化规范。FCC给出了超宽带信号的两种定义：一种为相对带宽大于20％的电磁波信号，另一种则基于绝对带宽，信号的10dB带宽大于500MHz即可。其表达式如下所示：Ultra-wideband technology is a carrier-free wireless communication technology with the characteristics of fast transmission rate, high ranging accuracy, low power consumption, and strong anti-multipath interference ability. In the 1960s, UWB was initially used for military purposes, and it was not until 2002 that the Federal Communications Commission (FCC) of the United States issued commercial specifications. The FCC gave two definitions of ultra-wideband signals: one is an electromagnetic wave signal with a relative bandwidth greater than 20%, and the other is based on absolute bandwidth, and the 10dB bandwidth of the signal is greater than 500MHz. The expression is as follows:
1.相对带宽大于20％，则信号满足：1. If the relative bandwidth is greater than 20%, the signal satisfies:
2.绝对带宽大于500MHz，则信号满足：2. If the absolute bandwidth is greater than 500MHz, the signal satisfies:
fH-fL≥500MHz； (17)fH -fL≥500MHz ; (17)
上式中，fH、fL是信号衰减为10dB时的上限截止频率、下限截止频率，fC为中心频率，计算公式为(fH-fL)/2。为了避免对其他无线波通信系统的相互干扰，FCC规定超宽带信号的规范工作频谱为3.1GHz-10.6GHz(可用频谱为7.5GHz)，辐射功率不超过41.25dBm/MHz。In the above formula,fH andfL are the upper and lower cutoff frequencies when the signal attenuation is 10dB,fC is the center frequency, and the calculation formula is (fH -fL )/2. In order to avoid mutual interference with other wireless wave communication systems, the FCC stipulates that the standard operating spectrum of ultra-wideband signals is 3.1GHz-10.6GHz (the available spectrum is 7.5GHz), and the radiation power does not exceed 41.25dBm/MHz.
本实施例使用Decawave公司的DW1000超宽带芯片开发了超宽带接收发射模块。DW1000是一款低功耗单芯片CMOS无线接收发射芯片，符合IEEE802.15.4-2011超宽带(UWB)标准，跨越了从3.5GHz到6.5GHz的6个射频频段，支持110kbps、850kbps、6.8Mbps的数据速率。This embodiment uses Decawave's DW1000 ultra-wideband chip to develop an ultra-wideband receiving and transmitting module. DW1000 is a low-power single-chip CMOS wireless receiving and transmitting chip that complies with the IEEE802.15.4-2011 ultra-wideband (UWB) standard, spans six radio frequency bands from 3.5GHz to 6.5GHz, and supports data rates of 110kbps, 850kbps, and 6.8Mbps.
如图8所示，其为DW1000的芯片结构图，DW1000由一个包含接收器和发送器的模拟前端和一个与主机连接的数字后端组成。接收器和发射器共用一个天线，可以通过选择开关来进行发送和接收模式的切换。接收器由RF模拟信号接收端和数字信号接收端组成，接收器可在低噪声放大器中放大接收到的信号，然后将其直接下变频为基带信号，最后基带信号被解调并导入到接收缓冲寄存器中，并通过SPI接口传输给主机控制器。与接收器类似，发射器由RF模拟信号发射端和数字信号发射端组成。发射器通过将数字编码的待发送数据通过模拟脉冲发生器来生成发送脉冲序列，之后由双平衡混频器上变频为合成器生成的载波，并按照IEEE 802.15.4-2011规定的UWB信道中心频率调制，最终得到的RF信号将会在发射之前进行放大处理。As shown in Figure 8, it is a chip structure diagram of DW1000. DW1000 consists of an analog front end including a receiver and a transmitter and a digital back end connected to the host. The receiver and the transmitter share an antenna, and the transmission and reception modes can be switched by selecting a switch. The receiver consists of an RF analog signal receiving end and a digital signal receiving end. The receiver can amplify the received signal in a low noise amplifier, and then directly down-convert it to a baseband signal. Finally, the baseband signal is demodulated and imported into the receiving buffer register, and transmitted to the host controller through the SPI interface. Similar to the receiver, the transmitter consists of an RF analog signal transmitting end and a digital signal transmitting end. The transmitter generates a transmission pulse sequence by passing the digitally encoded data to be transmitted through an analog pulse generator, and then up-converts it to a carrier generated by a synthesizer by a double-balanced mixer, and modulates it according to the UWB channel center frequency specified in IEEE 802.15.4-2011. The final RF signal will be amplified before transmission.
DW1000通过SPI与主机控制器进行通信。状态控制单元包含控制逻辑电路和寄存器组，寄存器组主要分为配置寄存器、状态寄存器、控制寄存器、数据缓冲寄存器和诊断寄存器。主机通过SPI接口访问和写入寄存器组来对DW1000进行控制。此外，DW1000还具有片上一次性可编程(OTP)存储器。该存储器可用于存储校准数据，例如发射功率电平、晶体初始频率误差调整和范围精度调整，这些调整值可以在需要时自动检索。The DW1000 communicates with the host controller via SPI. The state control unit contains control logic circuits and register groups, which are mainly divided into configuration registers, status registers, control registers, data buffer registers, and diagnostic registers. The host controls the DW1000 by accessing and writing register groups through the SPI interface. In addition, the DW1000 also has an on-chip one-time programmable (OTP) memory. This memory can be used to store calibration data, such as transmit power level, crystal initial frequency error adjustment, and range accuracy adjustment, and these adjustment values can be automatically retrieved when needed.
DW1000内部具有一个片上晶体振荡器，该晶振以38.4MHz的频率工作，DW1000支持以外部38.4MHz的振荡源来代替该片上晶振。由片上晶振器引出的RF PLL和CLKPLL两个锁相环时钟分支电路分别用于RF前端的信号生成、获取和数字后端的信号处理、IC控制。此外DW1000还有一个内部13kHz振荡器，用于低功耗睡眠状态的唤醒控制。主机接口包括与主机控制器通信的SPI接口和MAC功能单元，包括循环冗余码(FCS)生成、循环冗余校验(CRC)过程和接收帧过滤等。DW1000 has an on-chip crystal oscillator that operates at a frequency of 38.4MHz. DW1000 supports replacing the on-chip crystal oscillator with an external 38.4MHz oscillator source. The two phase-locked loop clock branch circuits, RF PLL and CLKPLL, derived from the on-chip crystal oscillator are used for signal generation and acquisition at the RF front end and signal processing and IC control at the digital back end. In addition, DW1000 also has an internal 13kHz oscillator for wake-up control in low-power sleep states. The host interface includes an SPI interface and a MAC functional unit that communicates with the host controller, including cyclic redundancy code (FCS) generation, cyclic redundancy check (CRC) process, and receive frame filtering.
对于基于测距技术的室内定位系统而言，测距信息的准确与否直接影响定位系统的定位精度。DW1000芯片支持双边双向测距(Double-Sided Two-Way Ranging，DS-TWR)方法，其作为传播时间(Time ofFlight，TOF)测距过程中常用的方法之一，以严格的时间对称约束来消除时钟漂移的影响，定位精度可达厘米级，因此采用SDS-TWR测距方法作为本实例的测距信息的获取方式。如图9所示，其为SDS-TWR的测距流程图，首先，待测目标节点发送Poll帧给参考节点，发送时间为t0；参考节点接收Poll帧，接收时间为a1，并于a2时刻发送Response帧给待测目标，计算出a1到a2的时间间隔treplyA(参考节点A的消息处理时延)；待测目标于t1时刻接收到Response帧，并在t2时刻发送Final帧，其中Final帧中包含了t0到t1的时间间隔troundT(待测目标T的消息往返时延)，以及t1到t2的时间间隔treplyT(待测目标T的消息处理时延)；参考节点于a3接收到Final帧，记录Final帧中的troundT和treplyT值，并计算出a2到a3的时间间隔troundA(参考节点A的消息往返时延)，此外，图9中t0到t3的时间间隔tC代表待测目标完成一次与参考节点的测距过程所需要的时间。此时，在参考节点处，通过计算出信号的飞行时间tprop，用tprop乘以信号的飞行速度c并可以计算出待测目标到参考节点的距离d。For indoor positioning systems based on ranging technology, the accuracy of ranging information directly affects the positioning accuracy of the positioning system. The DW1000 chip supports the Double-Sided Two-Way Ranging (DS-TWR) method, which is one of the commonly used methods in the Time of Flight (TOF) ranging process. It uses strict time symmetry constraints to eliminate the influence of clock drift, and the positioning accuracy can reach the centimeter level. Therefore, the SDS-TWR ranging method is used as the method for obtaining ranging information in this example. As shown in FIG9 , it is a ranging flow chart of SDS-TWR. First, the target node to be measured sends a Poll frame to the reference node at a sending time of t0 ; the reference node receives the Poll frame at a receiving time of a1 , and sends a Response frame to the target to be measured at a2 time, and calculates the time interval treplyA from a1 to a2 (the message processing delay of reference node A); the target to be measured receives the Response frame at t1 time, and sends a Final frame at t2 time, wherein the Final frame includes the time interval troundT from t0 to t1 (the message round trip delay of the target T to be measured), and the time interval treplyT from t1 to t2 (the message processing delay of the target T to be measured); the reference node receives the Final frame at a3 time, records the values of troundT and treplyT in the Final frame, and calculates the time interval troundA from a2 to a3 (the message round trip delay of reference node A). In addition, the time interval t roundT from t0 to t3 in FIG9 isC represents the time required for the target to complete a distance measurement process with the reference node. At this time, at the reference node, by calculating the flight time tprop of the signal, multiplying tprop by the flight speed c of the signal, the distance d from the target to the reference node can be calculated.
如将上述定位系统应用于室内场所下任一房间内的行人定位，根据统计经验，行人的行走的速度介于1.1-1.5m/s之间，因此设置待测目标三维坐标系下的运动速度分别为vx＝1m/s,vy＝1m/s,vz＝0m/s，则该待测目标的实际速度为在系统状态噪声的影响下，便可以模拟实际情况下的行人行走速度。系统的其他参数设置也仍和仿真分析中相同。仿真得出的基于速度时间测距模型下的taylor定位算法随着测距间隔时间δt变化的定位性能曲线图如图10所示。设计该室内定位系统的定位性能要求为RMSE≤0.5m，则要求任意两个参考节点间的测距间隔时间小于等于ts，而由上述DS-TWR测距的流程可知，待测目标与任一参考节点完成测距过程至少需要的时间为tc。因此，在该应用实例中，应设置任意两个参考节点的测距间隔时间为tc≤δt≤ts。If the above positioning system is applied to the positioning of pedestrians in any room in an indoor place, according to statistical experience, the walking speed of pedestrians is between 1.1-1.5m/s. Therefore, the movement speed of the target to be measured in the three-dimensional coordinate system is set to vx = 1m/s,vy = 1m/s, vz = 0m/s. The actual speed of the target to be measured is Under the influence of system state noise, the walking speed of pedestrians in actual situations can be simulated. The other parameter settings of the system are still the same as those in the simulation analysis. The positioning performance curve of the Taylor positioning algorithm based on the speed time ranging model obtained by simulation as the ranging interval time δt changes is shown in Figure 10. The positioning performance requirement of the indoor positioning system is designed to be RMSE≤0.5m, which requires the ranging interval time between any two reference nodes to be less than or equal to ts . From the above DS-TWR ranging process, it can be seen that the minimum time required for the target to be measured and any reference node to complete the ranging process is tc . Therefore, in this application example, the ranging interval time between any two reference nodes should be set to tc ≤δt ≤ts .
以上针对行人定位系统下参考节点测距间隔时间设计的简单例子，通过有效的测距间隔时间参数设计，不仅很好的满足了定位系统的性能要求，而且有利于参考节点的轮询调度。因此，可将本实施例内容应用于各种移动目标的室内定位系统中，不仅可以有效降低系统的定位误差，而且可以探究在满足不同室内定位系统性能要求的前提下，其任意两个参考节点间的测距间隔时间的参数设置问题，使待测目标在这段时间间隔内，可以移动满足系统定位性能的距离偏移量，进而使得对于移动目标定位系统的研究更加具有实际意义，可以为实际的定位系统搭建过程以及参考节点的轮询时间设置问题等提供理论基础和参数指标。The above is a simple example of the design of the reference node ranging interval time under the pedestrian positioning system. Through the effective ranging interval time parameter design, it not only satisfies the performance requirements of the positioning system well, but also facilitates the polling scheduling of the reference node. Therefore, the content of this embodiment can be applied to the indoor positioning system of various mobile targets, which can not only effectively reduce the positioning error of the system, but also explore the parameter setting problem of the ranging interval time between any two reference nodes under the premise of meeting the performance requirements of different indoor positioning systems, so that the target to be measured can move within this time interval. The distance offset that meets the system positioning performance, thereby making the research on the mobile target positioning system more practical, and can provide a theoretical basis and parameter indicators for the actual positioning system construction process and the reference node polling time setting problem.
实施例2：Embodiment 2:
本实施例用于提供一种室内移动目标定位系统，如图11所示，所述定位系统包括：This embodiment is used to provide an indoor mobile target positioning system, as shown in FIG11 , the positioning system includes:
模型改进模块M1，用于考虑多个参考节点的分时测距过程中待测目标的移动性，对经典室内定位系统模型重新进行系统建模，得到改进后室内定位系统模型；The model improvement module M1 is used to consider the mobility of the target to be measured in the time-sharing ranging process of multiple reference nodes, remodel the classic indoor positioning system model, and obtain an improved indoor positioning system model;
算法改进模块M2，用于根据所述改进后室内定位系统模型对经典室内定位算法进行改进，得到改进后室内定位算法；An algorithm improvement module M2 is used to improve the classic indoor positioning algorithm according to the improved indoor positioning system model to obtain an improved indoor positioning algorithm;
定位模块M3，用于利用所述改进后室内定位算法对所述改进后室内定位系统模型进行求解，得到所述待测目标的定位结果。The positioning module M3 is used to solve the improved indoor positioning system model by using the improved indoor positioning algorithm to obtain the positioning result of the target to be measured.
其中，改进后室内定位系统模型包括基于距离偏量的测距模型和基于速度时间的测距模型。Among them, the improved indoor positioning system model includes a ranging model based on distance deviation and a ranging model based on speed time.
本实施例设计了一种基于移动目标的室内定位系统，针对室内移动目标定位系统，当其在任一时刻对待测目标进行定位时，将该时刻多个参考节点对待测目标的分时测距以及在进行多组分时测距期间待测目标的移动性考虑在内，对经典室内定位系统模型进行了系统的重新建模，建立了基于距离偏量的测距模型和基于速度时间的测距模型，将距离偏量δd、待测目标的运动速度v和测距间隔时间δt等引入测距模型，使其更加适用于移动目标定位系统的实际应用；并基于上述测距模型，进行了经典taylor定位算法的重新推导和仿真研究，验证了基于上述系统模型下定位算法的有效性。此外，根据主要系统参数对定位性能的影响，探究在满足不同室内定位系统性能要求的前提下，对于不同运动速度的移动目标，其任意两个参考节点间的测距间隔时间的参数设置问题，使待测目标在这段时间间隔内，可以移动满足系统定位性能的距离偏移量，进而使得对于移动目标定位系统的研究更加具有实际意义，可以为实际的定位系统搭建过程以及参考节点的轮询时间设置问题等提供理论基础和参数指标。This embodiment designs an indoor positioning system based on a mobile target. For the indoor mobile target positioning system, when the system locates the target to be measured at any time, the time-sharing ranging of the target to be measured by multiple reference nodes at that time and the mobility of the target to be measured during the multi-group time-sharing ranging are taken into account. The classic indoor positioning system model is systematically remodeled, and a ranging model based on distance deviation and a ranging model based on speed time are established. The distance deviation δd , the moving speed v of the target to be measured and the ranging interval time δt are introduced into the ranging model, making it more suitable for the practical application of the mobile target positioning system; and based on the above ranging model, the classic Taylor positioning algorithm is re-derived and simulated, and the effectiveness of the positioning algorithm based on the above system model is verified. In addition, based on the impact of the main system parameters on the positioning performance, the problem of parameter setting of the ranging interval time between any two reference nodes for moving targets with different movement speeds is explored, under the premise of meeting the performance requirements of different indoor positioning systems. This allows the target to be measured to move a distance offset that meets the system positioning performance within this time interval, thereby making the research on the mobile target positioning system more practical, and can provide a theoretical basis and parameter indicators for the actual positioning system construction process and the polling time setting problem of the reference node.
本说明书中每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。对于实施例公开的系统而言，由于其与实施例公开的方法相对应，所以描述的比较简单，相关之处参见方法部分说明即可。Each embodiment in this specification focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处。综上所述，本说明书内容不应理解为对本发明的限制。This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210229828.9ACN114608580B (en) | 2022-03-10 | 2022-03-10 | Indoor mobile target positioning method and system |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210229828.9ACN114608580B (en) | 2022-03-10 | 2022-03-10 | Indoor mobile target positioning method and system |
| Publication Number | Publication Date |
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| CN114608580A CN114608580A (en) | 2022-06-10 |
| CN114608580Btrue CN114608580B (en) | 2024-08-27 |
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| CN202210229828.9AActiveCN114608580B (en) | 2022-03-10 | 2022-03-10 | Indoor mobile target positioning method and system |
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| CN106405492A (en)* | 2016-08-31 | 2017-02-15 | 广西科技大学 | Mobile target detection method based on acoustic waves and wireless positioning |
| CN108769969A (en)* | 2018-06-20 | 2018-11-06 | 吉林大学 | A kind of RFID indoor orientation methods based on depth confidence network |
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| CN109100683A (en)* | 2018-06-29 | 2018-12-28 | 福州大学 | Chan- weighted mass center indoor orientation method based on Kalman filtering |
| CN110248325B (en)* | 2019-04-22 | 2022-11-25 | 西安邮电大学 | A Bluetooth indoor positioning system based on signal multiple denoising |
| WO2020219692A1 (en)* | 2019-04-25 | 2020-10-29 | Nec Laboratories America, Inc. | Tracking indoor objects with inertial sensor measurements |
| CN112533149B (en)* | 2020-11-27 | 2022-06-07 | 桂林理工大学 | Moving target positioning algorithm based on UWB mobile node |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106405492A (en)* | 2016-08-31 | 2017-02-15 | 广西科技大学 | Mobile target detection method based on acoustic waves and wireless positioning |
| CN108769969A (en)* | 2018-06-20 | 2018-11-06 | 吉林大学 | A kind of RFID indoor orientation methods based on depth confidence network |
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| CN114608580A (en) | 2022-06-10 |
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