CN115615456A - Method and device for sensor error registration based on iterative nearest integer point set - Google Patents
Method and device for sensor error registration based on iterative nearest integer point setDownload PDFInfo
- Publication number
- CN115615456A CN115615456ACN202210929959.8ACN202210929959ACN115615456ACN 115615456 ACN115615456 ACN 115615456ACN 202210929959 ACN202210929959 ACN 202210929959ACN 115615456 ACN115615456 ACN 115615456A
- Authority
- CN
- China
- Prior art keywords
- target
- error
- measurement
- sensor
- equation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/40—Means for monitoring or calibrating
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Mathematical Optimization (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Remote Sensing (AREA)
- Computing Systems (AREA)
- Algebra (AREA)
- Radar, Positioning & Navigation (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Manufacturing & Machinery (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
Description
Translated fromChinese技术领域technical field
本申请属于飞机机载多源信息融合技术领域，特别涉及一种基于迭 代最邻近的整数点集传感器误差配准方法及设备。The application belongs to the field of aircraft airborne multi-source information fusion technology, and in particular relates to a method and device for sensor error registration based on iterative nearest integer point sets.
背景技术Background technique
飞机机载多源信息融合是指通过雷达、红外、电子对抗等多种传感 器获得目标量测信息，并将多源信息进行分层处理和整合，从而有助于 扩展时空覆盖范围、提升目标精度、优化传感器使用策略。传感器误差 配准是多源信息融合的首要环节，是指在统一的时空体系下通过对传感 器量测信息中的规律性系统误差进行实时估计与补偿，以减少对目标航 迹跟踪精度的影响。Aircraft airborne multi-source information fusion refers to the acquisition of target measurement information through radar, infrared, electronic countermeasures and other sensors, and the multi-source information is processed and integrated hierarchically, which helps to expand the space-time coverage and improve target accuracy , Optimizing the sensor usage strategy. Sensor error registration is the primary link of multi-source information fusion, which refers to the real-time estimation and compensation of regular systematic errors in sensor measurement information in a unified space-time system to reduce the impact on the target track tracking accuracy.
现有的传感器误差配准方法主要包括离线估计和在线估计两类。离 线估计通常只适用于传感器系统误差恒定的情况，对于非合作目标 (Non-CooperativeTarget)无效；在线估计则需要对系统误差估计和航迹 滤波过程解耦合处理，同时需要系统模型足够精确，对于算法应用而言 较为苛刻。The existing sensor error registration methods mainly include offline estimation and online estimation. Offline estimation is usually only suitable for the situation where the sensor system error is constant, and is invalid for non-cooperative targets (Non-Cooperative Target); online estimation requires decoupling of system error estimation and track filtering process, and requires the system model to be accurate enough. application is more demanding.
发明内容Contents of the invention
本申请的目的是提供了一种基于迭代最邻近整数点集的传感器误差 配准方法，以解决或减轻背景技术中的至少一个问题。The purpose of this application is to provide a sensor error registration method based on iterative nearest integer point sets to solve or alleviate at least one of the problems in the background art.
本申请的技术方案是：一种基于迭代最邻近整数点集的传感器误差 配准方法，所述方法包括：The technical scheme of the present application is: a kind of sensor error registration method based on iterative nearest integer point set, described method comprises:
构建用于描述机动目标航迹状态的方程，所述机动目标航迹状态的 方程通过线性递推方程表示，所述线性递推方程含有零均值随机过程噪 声；Construct the equation that is used to describe maneuvering target track state, the equation of described maneuvering target track state is represented by linear recurrence equation, and described linear recurrence equation contains zero-mean stochastic process noise;
构建用于描述传感器对目标量测的方程，所述传感器对目标量测的 方程含有量均值随机量测噪声及稳定的系统误差；Constructing an equation for describing the measurement of the target by the sensor, the equation of the measurement of the target by the sensor contains the random measurement noise of the quantity mean value and the stable systematic error;
基于对合作目标的量测点集，采用最小均方误差估计方法解算得到 系统误差估计；Based on the measurement point set of the cooperative target, the system error estimate is obtained by using the minimum mean square error estimation method;
基于上一时刻的目标航迹状态估计及其误差协方差，代入机动目标 航迹状态方程进行推算，实现目标的实时航迹预测；Based on the target track state estimation and its error covariance at the last moment, it is substituted into the maneuvering target track state equation for calculation, so as to realize the real-time track prediction of the target;
采用卡尔曼滤波算法，在得到传感器对目标的量测后进行航迹滤波 更新，进而得到当前时刻的目标航迹状态估计及其误差协方差；The Kalman filter algorithm is used to update the track filter after the measurement of the target by the sensor, and then obtain the target track state estimation and its error covariance at the current moment;
从传感器目标量测中减去系统误差估计实现系统误差补偿，将补偿 后的目标量测重新代入航迹滤波残差中，获得当前时刻的目标航迹状态 估计值，最终获得对目标航迹的无偏跟踪结果。Subtracting the system error estimate from the sensor target measurement realizes system error compensation, and resubstituting the compensated target measurement into the track filtering residual to obtain the target track state estimate at the current moment, and finally obtain the target track state estimate Unbiased tracking of results.
进一步的，所述线性递推方程为：x(k)＝A(k)x(k-1)+w(k)Further, the linear recurrence equation is: x(k)=A(k)x(k-1)+w(k)
式中，k为采样时刻，x(k)、x(k-1)分别为当前时刻k和上一时刻k-1的目 标运动状态，A(k)为状态矩阵，w(k)为零均值、协方差为Q的随机过程噪 声。In the formula, k is the sampling time, x(k) and x(k-1) are the target motion states at the current time k and the previous time k-1 respectively, A(k) is the state matrix, and w(k) is zero Random process noise with mean and covariance Q.
进一步的，所述传感器对目标量测的方程为：Further, the equation for the sensor to measure the target is:
zi(k)＝Hi(k)x(k)+ei+vi(k)zi (k)=Hi (k)x(k)+ei +vi (k)
其中，i为传感器标号，zi(k)为目标量测，Hi(k)为量测矩阵，vi(k)为零 均值、协方差为R的随机量测噪声，ei为系统误差。Among them, i is the sensor label, zi (k) is the target measurement, Hi (k) is the measurement matrix, vi (k) is the random measurement noise with zero mean and covariance R, ei is the system error.
进一步的，在二维平面，针对测距及测角特性，所述量测矩阵为
其中，xs、ys分别为传感器对目标量测点在平面直角坐标系的X轴坐 标、Y轴坐标，er、eθ分别为传感器对目标量测在距离维度、方位角维度 的系统误差。Among them, xs and ys are the X-axis coordinates and Y-axis coordinates of the sensor-to-target measurement point in the plane Cartesian coordinate system, respectively, and er and eθ are the distance and azimuth dimensions of the sensor-to-target measurement system error.
进一步的，得到所述系统误差估计的过程包括：Further, the process of obtaining the system error estimate includes:
基于对同一合作目标的量测点集，m次重复量测集合Z为 Z＝{zi,1,zi,2,...,zi,m}；Based on the measurement point set for the same cooperative target, the m-times repeated measurement set Z is Z={zi,1 ,zi,2 ,...,zi,m };
采用最小均方误差估计算法，通过解算获得使系统误差估计值
进一步的，在实现目标的实时航迹预测中，上一时刻k-1的目标航迹 状态估计满足时间更新方程：
其中，
进一步的，所述卡尔曼滤波算法中，卡尔曼滤波状态更新方程为：Further, in the Kalman filter algorithm, the Kalman filter state update equation is:
当前时刻的目标航迹状态估计及其误差协方差分别为：The target track state estimation and its error covariance at the current moment are:
P(k)＝[I-G(k)*Hi(k)]P(k|k-1)P(k)＝[IG(k)*Hi (k)]P(k|k-1)
其中，resi(k)为滤波残差，G(k)为滤波增益；Among them, resi (k) is the filter residual, G (k) is the filter gain;
进一步的，补偿后的目标量测
补偿后的滤波残差
重新代入航迹滤波残差方程后，当前时刻的目标航迹状态估计值为：After resubstituting the residual equation of the track filter, the estimated value of the target track state at the current moment is:
从而最终获得对目标航迹的无偏跟踪结果。In this way, the unbiased tracking result of the target track can be finally obtained.
此外，本申请还提供了一种设备，所述设备包括：In addition, the present application also provides a kind of equipment, and described equipment comprises:
存储器，用于存储计算机软件程序；memory for storing computer software programs;
处理器，用于读取并执行所述计算机软件程序，进而实现如上任一 项所述的基于迭代最邻近整数点集的传感器误差配准方法。A processor is used to read and execute the computer software program, so as to realize the sensor error registration method based on iterative nearest integer point set as described in any one of the above.
最后，本申请还提供了一种非暂态计算机可读存储介质，所述存储 介质中存储有用于实现如上任一项所述的基于迭代最邻近整数点集的传 感器误差配准方法的计算机软件程序。Finally, the present application also provides a non-transitory computer-readable storage medium, which stores computer software for implementing the sensor error registration method based on iterative nearest integer point sets as described in any one of the above program.
与现有技术相比，本申请提供的方法实现了对传感器固有的规律性 系统误差的有效辨识和估计，保证了目标航迹跟踪的无偏特性，提升了 精度，保证了目标航迹跟踪的全局一致性。Compared with the existing technology, the method provided by this application realizes the effective identification and estimation of the inherent regular system error of the sensor, ensures the unbiased characteristics of the target track tracking, improves the accuracy, and ensures the accuracy of the target track tracking. global consistency.
附图说明Description of drawings
为了更清楚地说明本申请提供的技术方案，下面将对附图作简单地 介绍。显而易见地，下面描述的附图仅仅是本申请的一些实施例。In order to illustrate the technical scheme provided by the present application more clearly, the accompanying drawings will be briefly introduced below. Apparently, the drawings described below are only some embodiments of the present application.
图1为本申请中传感器对目标量测的整体点集示意。FIG. 1 is a schematic diagram of an overall point set of a sensor measuring a target in this application.
图2为本申请中基于迭代最近邻整体点集的传感器误差配准方法流 程图。Fig. 2 is a flow chart of the sensor error registration method based on iterative nearest neighbor overall point set in the present application.
图3为本申请中的电子设备组成示意图。FIG. 3 is a schematic diagram of the composition of the electronic device in this application.
图4为本申请中的计算机可读存储介质组成示意图。FIG. 4 is a schematic diagram of the composition of a computer-readable storage medium in this application.
具体实施方式detailed description
为使本申请实施的目的、技术方案和优点更加清楚，下面将结合本 申请实施例中的附图，对本申请实施例中的技术方案进行更加详细的描 述。In order to make the purposes, technical solutions and advantages of the application more clear, the technical solutions in the embodiments of the application will be described in more detail below in conjunction with the drawings in the embodiments of the application.
本申请提出的一种基于迭代最近邻的整体点集传感器误差配准方 法，通过对目标实测点集的整体利用，获得具有最小均方误差意义的系 统误差最优估计值，贯串到以卡尔曼滤波为框架的目标航迹迭代更新过 程。本申请的方法能够提高机动目标航迹跟踪的精度和稳定性，有效抑 制系统误差的影响。This application proposes an iterative nearest neighbor based overall point set sensor error registration method, through the overall use of the target measured point set, to obtain the optimal estimate of the system error with the meaning of the minimum mean square error, through to the Kalman Filtering is an iterative update process of the frame's target track. The method of the present application can improve the accuracy and stability of maneuvering target track tracking, and effectively suppress the influence of system errors.
如图1所示，本发明提供的基于迭代最近邻的整体点集传感器误差配 准方法包括：As shown in Figure 1, the overall point set sensor error registration method based on iterative nearest neighbor provided by the present invention comprises:
S1、构建描述机动目标航迹状态的数学模型或方程，其中，机动目 标航迹状态可通过线性递推方程表示，含有零均值随机过程噪声。S1. Construct a mathematical model or equation describing the track state of the maneuvering target, wherein the track state of the maneuvering target can be expressed by a linear recurrence equation, containing zero-mean random process noise.
本申请中采用如下方程描述机动目标航迹状态：In this application, the following equation is used to describe the track state of the maneuvering target:
x(k)＝A(k)x(k-1)+w(k)x(k)=A(k)x(k-1)+w(k)
其中，k为采样时刻，x(k)、x(k-1)分别为采样时刻k和上一采样时刻k-1 时的目标运动状态，A(k)为状态矩阵，w(k)为零均值、协方差为Q的随机 过程噪声。Among them, k is the sampling time, x(k) and x(k-1) are the target motion states at the sampling time k and the previous sampling time k-1 respectively, A(k) is the state matrix, and w(k) is Random process noise with zero mean and covariance Q.
S2、构建用于描述传感器对目标量测的方程。S2. Construct an equation for describing the measurement of the target by the sensor.
本申请中采用如下方程描述传感器对目标的量测：In this application, the following equation is used to describe the measurement of the sensor to the target:
zi(k)＝Hi(k)x(k)+ei+vi(k)zi (k)=Hi (k)x(k)+ei +vi (k)
其中，i为传感器标号，zi(k)为目标量测，Hi(k)为量测矩阵，vi(k)为零 均值、协方差为R的随机量测噪声，ei为系统误差。Among them, i is the sensor label, zi (k) is the target measurement, Hi (k) is the measurement matrix, vi (k) is the random measurement noise with zero mean and covariance R, ei is the system error.
其中，在二维平面，分别针对测距、测角特性，将量测矩阵和系统 误差分别定义为：
式中，xs、ys分别为传感器对目标量测点在平面直角坐标系的X轴坐 标、Y轴坐标，er、eθ分别为传感器对目标量测在距离维度、方位角维度 的系统误差。In the formula, xs and ys are the X-axis coordinates and Y-axis coordinates of the sensor to the target measurement point in the plane Cartesian coordinate system respectively, er and eθ are the distance dimension and azimuth dimension of the sensor to the target measurement point respectively system error.
传感器对目标的量测含有零均值随机量测噪声以及稳定的系统误 差，分别表现为量测点集的离散分布程度以及量测值与真实值的偏离程 度，二者相互独立。The measurement of the target by the sensor contains zero-mean random measurement noise and stable system error, which are represented by the degree of discrete distribution of the measurement point set and the degree of deviation between the measured value and the true value, and the two are independent of each other.
如图2所示，每个散点表示传感器通过上式对目标的一次量测结果， 共同构成该目标的整体点集。基于空间最近邻准则，构建出以量测均值 为圆心的整体点集包络圆，其与目标真值之间的位置关系，表征了整体 点集的离散分布程度和偏离真值程度。As shown in Figure 2, each scatter point represents a measurement result of the target by the sensor through the above formula, which together constitute the overall point set of the target. Based on the spatial nearest neighbor criterion, the enveloping circle of the overall point set with the measurement mean as the center is constructed, and the positional relationship between it and the target true value characterizes the degree of discrete distribution and the degree of deviation from the true value of the overall point set.
S3、系统误差估计：基于对合作目标(CooperativeTarget)的量测点 集，采用最小均方误差估计算法解算得到系统误差估计。S3. System error estimation: Based on the measurement point set of the cooperative target (CooperativeTarget), use the minimum mean square error estimation algorithm to solve the system error estimation.
基于对同一合作目标的量测点集，m次重复量测集合Z为：Based on the measurement point set for the same cooperative target, the m-times repeated measurement set Z is:
Z＝{zi,1,zi,2,...,zi,m}；Z＝{zi,1 ,zi,2 ,...,zi,m };
采用最小均方误差估计算法，通过解算获得使系统误差估计值
S4、目标实时航迹预测：基于上一时刻k-1的目标航迹状态估计及其 误差协方差，代入机动目标航迹状态方程进行推算，实现目标的实时航 迹预测。S4. Target real-time track prediction: Based on the target track state estimation and error covariance of k-1 at the last moment, it is substituted into the maneuvering target track state equation to calculate, so as to realize the real-time track prediction of the target.
其中，上一时刻的目标航迹状态估计满足时间更新方程：Among them, the target track state estimation at the last moment satisfies the time update equation:
上一时刻的误差协方差满足时间更新方程：The error covariance at the last moment satisfies the time update equation:
P(k|k-1)＝A(k)P(k-1)AT(k)+Q；P(k|k-1)=A(k)P(k-1)AT (k)+Q;
其中，
S5、航迹滤波更新：采用卡尔曼滤波算法，在得到传感器对目标的 量测后进行迭代求解，输出当前时刻的目标航迹状态估计及其误差协方 差。S5. Track filter update: Kalman filter algorithm is used to iteratively solve after the measurement of the target by the sensor, and output the target track state estimate and its error covariance at the current moment.
其中，卡尔曼滤波状态更新方程包括：Among them, the Kalman filter state update equation includes:
在得到传感器对目标的量测后进行迭代求解：Iteratively solve after getting the measurement of the sensor to the target:
P(k)＝[I-G(k)*Hi(k)]P(k|k-1)P(k)＝[IG(k)*Hi (k)]P(k|k-1)
其中，resi(k)为滤波残差，G(k)为滤波增益；
S6、系统误差补偿：通过从传感器对目标量测中减去系统误差估计 值实现系统误差补偿，将补偿后的目标量测重新代入航迹滤波残差计算 公式，获得对目标航迹的无偏跟踪结果。S6. System error compensation: the system error compensation is realized by subtracting the estimated value of the system error from the target measurement by the sensor, and resubstituting the compensated target measurement into the residual calculation formula of the track filter to obtain an unbiased target track Tracking Results.
具体过程包括：The specific process includes:
补偿后的目标量测为
补偿后的滤波残差为
为空间配准后的量测重新代入航迹滤波残差后，当前时刻的目标航 迹状态估计值为
从而最终获得对目标航迹的无偏跟踪结果。In this way, the unbiased tracking result of the target track can be finally obtained.
与现有技术相比，本申请提供的方法实现了对传感器固有的规律性 系统误差的有效辨识和估计，保证了目标航迹跟踪的无偏特性，提升了 精度，保证了目标航迹跟踪的全局一致性。Compared with the existing technology, the method provided by this application realizes the effective identification and estimation of the inherent regular system error of the sensor, ensures the unbiased characteristics of the target track tracking, improves the accuracy, and ensures the accuracy of the target track tracking. global consistency.
请参阅图3，图3为本申请实施例提供的电子设备或机载设备的实施 例示意图。Referring to Fig. 3, Fig. 3 is a schematic diagram of an embodiment of an electronic device or an airborne device provided by an embodiment of the present application.
如图3所示，本申请该实施例提供了一种电子设备500，包括存储器 510、处理器520及存储在存储器520上并可在处理器520上运行的计算 机程序511，处理器520执行计算机程序511时实现以下步骤：As shown in FIG. 3 , this embodiment of the present application provides an
构建用于描述机动目标航迹状态的方程，所述机动目标航迹状态的 方程通过线性递推方程表示，所述线性递推方程含有零均值随机量测噪 声及系统误差；Constructing an equation for describing the state of the maneuvering target track, the equation of the state of the maneuvering target track is represented by a linear recurrence equation, and the linear recurrence equation contains zero-mean random measurement noise and systematic error;
构建用于描述传感器对目标量测的方程；Construct the equations that describe the sensor's measurement of the target;
基于对合作目标的量测点集，采用最小均方误差估计方法解算得到 系统误差估计；Based on the measurement point set of the cooperative target, the system error estimate is obtained by using the minimum mean square error estimation method;
基于上一时刻的目标航迹状态估计及其误差协方差，代入机动目标 航迹状态方程进行推算，实现目标的实时航迹预测；Based on the target track state estimation and its error covariance at the last moment, it is substituted into the maneuvering target track state equation for calculation, so as to realize the real-time track prediction of the target;
采用卡尔曼滤波算法，在得到传感器对目标的量测后进行航迹滤波 更新，进而得到当前时刻的目标航迹状态估计及其误差协方差；The Kalman filter algorithm is used to update the track filter after the measurement of the target by the sensor, and then obtain the target track state estimation and its error covariance at the current moment;
从传感器目标量测中减去系统误差估计实现系统误差补偿，将补偿 后的目标量测重新代入航迹滤波残差中，获得当前时刻的目标航迹状态 估计值，最终获得对目标航迹的无偏跟踪结果。Subtracting the system error estimate from the sensor target measurement realizes system error compensation, and resubstituting the compensated target measurement into the track filtering residual to obtain the target track state estimate at the current moment, and finally obtain the target track state estimate Unbiased tracking of results.
请参阅图4，图4为本申请实施例提供的一种计算机可读存储介质的 实施例示意图。Please refer to FIG. 4, which is a schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present application.
如图4所示，本申请该实施例提供的计算机可读存储介质600，其上 存储有计算机程序611，该计算机程序611被处理器执行时实现如下步骤：As shown in Figure 4, the computer-
构建用于描述机动目标航迹状态的方程，所述机动目标航迹状态的 方程通过线性递推方程表示，所述线性递推方程含有零均值随机量测噪 声及系统误差；Constructing an equation for describing the state of the maneuvering target track, the equation of the state of the maneuvering target track is represented by a linear recurrence equation, and the linear recurrence equation contains zero-mean random measurement noise and systematic error;
构建用于描述传感器对目标量测的方程；Construct the equations that describe the sensor's measurement of the target;
基于对合作目标的量测点集，采用最小均方误差估计方法解算得到 系统误差估计；Based on the measurement point set of the cooperative target, the system error estimate is obtained by using the minimum mean square error estimation method;
基于上一时刻的目标航迹状态估计及其误差协方差，代入机动目标 航迹状态方程进行推算，实现目标的实时航迹预测；Based on the target track state estimation and its error covariance at the last moment, it is substituted into the maneuvering target track state equation for calculation, so as to realize the real-time track prediction of the target;
采用卡尔曼滤波算法，在得到传感器对目标的量测后进行航迹滤波 更新，进而得到当前时刻的目标航迹状态估计及其误差协方差；The Kalman filter algorithm is used to update the track filter after the measurement of the target by the sensor, and then obtain the target track state estimation and its error covariance at the current moment;
从传感器目标量测中减去系统误差估计实现系统误差补偿，将补偿 后的目标量测重新代入航迹滤波残差中，获得当前时刻的目标航迹状态 估计值，最终获得对目标航迹的无偏跟踪结果。Subtracting the system error estimate from the sensor target measurement realizes system error compensation, and resubstituting the compensated target measurement into the track filtering residual to obtain the target track state estimate at the current moment, and finally obtain the target track state estimate Unbiased tracking of results.
需要说明的是，在上述实施例中，对各个实施例的描述都各有侧重， 某个实施例中没有详细描述的部分，可以参见其它实施例的相关描述。It should be noted that, in the foregoing embodiments, the descriptions of each embodiment have their own emphases, and for parts that are not described in detail in a certain embodiment, reference may be made to relevant descriptions of other embodiments.
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、 或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实 施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在 一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包 括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, systems, or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机 程序产品的流程图和/或方框图来描述。应理解可由计算机程序指令实现 流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的 流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用 计算机、嵌入式计算机或者其他可编程数据处理设备的处理器以产生一 个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指 令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多 个方框中指定的功能的装置。The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each process and/or block in the flowchart and/or block diagrams, and a combination of processes and/or blocks in the flowchart and/or block diagrams can be realized by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce a machine for A device for realizing the functions specified in one or more procedures of a flowchart and/or one or more blocks of a block diagram.
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处 理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可 读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程 图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备 上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算 机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于 实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指 定的功能的步骤。These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不 局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内， 可轻易想到的变化或替换，都应涵盖在本申请的保护范围之内。因此， 本申请的保护范围应以所述权利要求的保护范围为准。The above is only a specific implementation of the application, but the scope of protection of the application is not limited thereto. Any person familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the application. All should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be determined by the protection scope of the claims.
Claims (10)
Translated fromChinese
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210929959.8ACN115615456A (en) | 2022-08-03 | 2022-08-03 | Method and device for sensor error registration based on iterative nearest integer point set |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210929959.8ACN115615456A (en) | 2022-08-03 | 2022-08-03 | Method and device for sensor error registration based on iterative nearest integer point set |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115615456Atrue CN115615456A (en) | 2023-01-17 |
Family
ID=84856296
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210929959.8APendingCN115615456A (en) | 2022-08-03 | 2022-08-03 | Method and device for sensor error registration based on iterative nearest integer point set |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115615456A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117323002A (en)* | 2023-11-30 | 2024-01-02 | 北京万特福医疗器械有限公司 | A neuroendoscopic surgery visualization system based on mixed reality technology |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111829505A (en)* | 2020-01-08 | 2020-10-27 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | A multi-sensor track quality extrapolation track fusion method |
- 2022
- 2022-08-03CNCN202210929959.8Apatent/CN115615456A/enactivePending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111829505A (en)* | 2020-01-08 | 2020-10-27 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | A multi-sensor track quality extrapolation track fusion method |
Non-Patent Citations (1)
| Title |
|---|
| 段恒宇 等: "目标航迹融合空间误差估计与补偿算法研究", 电光与控制, vol. 28, no. 6, 30 June 2021 (2021-06-30), pages 25 - 28* |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117323002A (en)* | 2023-11-30 | 2024-01-02 | 北京万特福医疗器械有限公司 | A neuroendoscopic surgery visualization system based on mixed reality technology |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110927740B (en) | Mobile robot positioning method | |
| CN110895408B (en) | Autonomous positioning method and device and mobile robot | |
| CN116047495B (en) | A State Transformation Fusion Filtering Tracking Method for Three-coordinate Radar | |
| Xie et al. | An improved algorithm based on particle filter for 3D UAV target tracking | |
| CN117367419A (en) | Robot positioning method, apparatus and computer readable storage medium | |
| CN116088503A (en) | Dynamic obstacle detection method and robot | |
| CN113483762A (en) | Pose optimization method and device | |
| CN116929338A (en) | Map construction method, device and storage medium | |
| Liu et al. | Exploiting radio fingerprints for simultaneous localization and mapping | |
| Jiang et al. | 3D SLAM based on NDT matching and ground constraints for ground robots in complex environments | |
| CN115328168A (en) | Mobile robot synchronous positioning and mapping method and system based on adaptive strong tracking | |
| CN110442014B (en) | A Position-Based RFID Servo Method for Mobile Robots | |
| CN118965475B (en) | A Maneuvering Target Cluster Tracking Method Based on Gaussian Process | |
| CN115615456A (en) | Method and device for sensor error registration based on iterative nearest integer point set | |
| Pei et al. | A decorrelated distributed EKF-SLAM system for the autonomous navigation of mobile robots | |
| CN111735443B (en) | Dense target track correlation method based on assignment matrix | |
| CN119124146A (en) | A dynamic environment positioning method based on lidar and IMU | |
| CN109375159B (en) | Pure orientation weighting constraint total least square positioning method | |
| CN117405118A (en) | A multi-sensor fusion mapping method, system, equipment and storage medium | |
| CN112581616B (en) | Nearest neighbor UKF-SLAM method based on sequential block filtering | |
| CN108732533B (en) | Self-positioning and self-tracking method for single-radiation-source maneuvering flying target | |
| Fuchun et al. | Research on indoor robot SLAM of RBPF improved with geometrical characteristic localization | |
| Xu et al. | Multi-sensor fusion framework based on GPS state detection | |
| CN119492380B (en) | A multi-robot consistent collaborative positioning method and system based on transformation | |
| CN119846606B (en) | Laser radar calibration method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication | Application publication date:20230117 |