多源传感器箱粒子LMB滤波算法

张永权; 李志彬; 张文博; 苏镇镇

doi:10.19665/j.issn1001-2400.20240104

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多源传感器箱粒子LMB滤波算法

信息与通信工程 | 更新时间：2024-11-13

- 多源传感器箱粒子LMB滤波算法
- Multi-source sensor box particle LMB filtering algorithm
- 西安电子科技大学学报 2024年51卷第4期页码：51-66
- 作者机构：
  
  1. 西安电子科技大学电子工程学院,陕西西安 710071
  2. 西安电子科技大学计算机科学与技术学院,陕西西安 710071
- 作者简介：
  
  [ "张永权(1985—),男,副教授,E-mail:[email protected]" ]
  [ "李志彬(1999—),男,西安电子科技大学硕士研究生,E-mail:[email protected]" ]
  张文博(1985—),男,副教授,E-mail:[email protected]
  [ "苏镇镇(1993—),男,讲师,E-mail:[email protected]" ]
- 基金信息：
  
  国家自然科学基金(62276204);国家自然科学基金(62306222);博士后特别资助(2020T130494);陕西省自然科学基金(2022JM-340);陕西省自然科学基金(2023-JC-QN-0710)
- DOI：10.19665/j.issn1001-2400.20240104
  中图分类号： V219
- 纸质出版日期：2024-08-20，
  
  网络出版日期：2024-02-27，
  
  收稿日期：2023-10-11，
- 稿件说明：
移动端阅览
张永权, 李志彬, 张文博, 等. 多源传感器箱粒子LMB滤波算法[J]. 西安电子科技大学学报, 2024,51(4):51-66.

Yongquan ZHANG, Zhibin LI, Wenbo ZHANG, et al. Multi-source sensor box particle LMB filtering algorithm. [J]. Journal of Xidian University, 2024,51(4):51-66.
张永权, 李志彬, 张文博, 等. 多源传感器箱粒子LMB滤波算法[J]. 西安电子科技大学学报, 2024,51(4):51-66. DOI： 10.19665/j.issn1001-2400.20240104.

Yongquan ZHANG, Zhibin LI, Wenbo ZHANG, et al. Multi-source sensor box particle LMB filtering algorithm. [J]. Journal of Xidian University, 2024,51(4):51-66. DOI： 10.19665/j.issn1001-2400.20240104.

摘要

随着复杂跟踪场景的大量涌现

常规多源传感器多目标跟踪算法存在计算量大、跟踪精度低、无法估计目标航迹等不足

已无法满足现代战争的需求。笔者以主动传感器和被动传感器组成的多源传感器系统为背景

重点研究多源传感器多目标跟踪问题的实现算法。针对“多主动+多被动”多源传感器系统量测无法充分融合且整体算法计算复杂度较高的问题

提出一种多源传感器箱粒子标签多伯努利(MS-BPF-LMB)滤波算法。首先

对传感器依据不同主动传感器进行分组

即将所有传感器划分为若干“单主动+多被动”传感器组;然后

通过并行运算

对各传感器组运用基于角度关联的多传感器信息融合算法

得到跟踪所需的有效量测;最后

在跟踪滤波阶段

通过引入箱粒子滤波数值计算方法

将获取的量测点划分为若干箱粒子

并对箱粒子滤波下的多传感器量测更新系数进行重新定义

以较低的计算复杂度实现LMB(Labeled multi-Bernoulli

LMB)滤波。仿真结果表明

所提算法在保证跟踪精度的前提下

误差明显降低且算法复杂度下降约40%

能够有效处理异构数据多源信息融合问题。

Abstract

With the emergence of a large number of complex tracking scenarios

conventional multi-source sensor multi-target tracking algorithms have shortcomings of high computational complexity

low tracking accuracy

and inability to estimate target trajectories

making them unable to meet the needs of modern warfare.In this paper

we focus on the implementation of the multi-source sensor tracking problem with the background of the multi-source sensor system composed of active and passive sensors.For the problem of a “multi-active+multi-passive” multi-source sensor system that the measurement cannot be fully integrated and the overall algorithm complexity is high

a multi-source sensor box particle labeled multi-Bernoulli(MS-BPF-LMB)filtering algorithm is proposed.First

the sensors are grouped according to different active sensors

i.e.

all sensors are divided into several "single active + multiple passive" sensor groups;and then

through parallel operations

a multi-sensor information fusion method based on angle correlation is applied to each sensor group to obtain the effective measurements required for tracking.Finally

in the tracking filtering stage

the obtained measurement points are divided into several box particles by introducing the box particle filtering numerical calculation method

and the update coefficients of multi-sensor measurements under box particle filtering are redefined to achieve LMB filtering with a low computational complexity.Simulation results show that the proposed method can effectively deal with the problem of multi-source information fusion of heterogeneous data by significantly reducing the error and decreasing the complexity of the algorithm by about 40% on the basis of maintaining the tracking accuracy of the target.

关键词

目标跟踪传感器数据融合信息融合箱粒子滤波标签多伯努利滤波

Keywords

target trackingsensor data fusioninformation fusionbox particle filteringlabeled multi-Bernoulli filtering

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