基于分布式PMHT的多传感器多目标跟踪

doi:10.12305/j.issn.1001-506X.2024.07.02

系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (7): 2184-2190.doi: 10.12305/j.issn.1001-506X.2024.07.02

• 电子技术 • 上一篇

基于分布式PMHT的多传感器多目标跟踪

姚思亦¹, 李万春¹^,*, 高林¹, 张花国¹, 胡航玮²

1. 电子科技大学信息与通信工程学院, 四川成都 611731
2. 北京机电工程研究所, 北京 100074

收稿日期:2023-05-18 出版日期:2024-06-28 发布日期:2024-07-02
通讯作者: 李万春
作者简介:姚思亦(1999—), 男, 硕士研究生, 主要研究方向为目标跟踪
李万春(1978—), 男, 副教授, 博士, 主要研究方向为统计信号处理、非合作信号处理技术、无源定位技术
高林(1990—), 男, 副教授, 博士, 主要研究方向为分布式感知、多目标跟踪、非合作信号处理
张花国(1979—), 男, 副教授, 博士, 主要研究方向为阵列信号处理、信号盲估计
胡航玮(1994—), 男, 研究员, 硕士, 主要研究方向为协同探测、电子对抗

Multi-sensor multi-target tracking based on distributed PMHT

Siyi YAO¹, Wanchun LI¹^,*, Lin GAO¹, Huaguo ZHANG¹, Hangwei HU²

1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2. Beijing Institute of Mechanical and Electrical Engineering, Beijing 100074, China

Received:2023-05-18 Online:2024-06-28 Published:2024-07-02
Contact: Wanchun LI

摘要/Abstract

摘要：

在目标跟踪领域，概率多假设跟踪(probability multiple hypothesis tracking, PMHT)算法作为一种批处理算法，计算量远远小于传统的多假设跟踪算法。当前，PMHT算法的应用受限于集中式处理，本文首先在传统算法的基础上对传感器网络下的算法似然进行了推导，得到多传感器算法下的关联后参数，接着基于共识性处理策略进行了混合共识，最后使用卡尔曼滤波完成了对目标参数的后验估计，使得PMHT算法能够被应用于不包含融合中心的全分布式传感器网络多目标跟踪。实验结果表明，在不同的杂波密度下，分布式PMHT在跟踪误差上相对于单传感器算法有着90%以上的改善效果，与集中式算法相比跟踪性能接近且运算速度更快。

关键词: 多目标跟踪, 概率多假设跟踪, 一致性共识, 集中式状态估计, 分布式状态估计

Abstract:

In the field of target tracking, probability multiple hypothesis tracking (PMHT) algorithm, as a batch processing algorithm, has much less computation than the traditional multiple hypothesis tracking algorithm. Currently, the application of PMHT algorithm is limited by centralized processing. On the basis of the traditional algorithm, this study firstly derives the algorithm likelihood under sensor network to obtain the post-correlation parameter under multi-sensor algorithm, followed by hybrid consensus based on the consensus processing strategy, and finally the posteriori estimation of the target parameters is accomplished by using Kalman filtering. This study enables the PMHT algorithm to be applied to the fully distributed sensor network without fusion centers. The experimental results show that under different clutter densities, the distributed PMHT has more than 90% improvement in tracking error compared to the single-sensor algorithm. Distributed PMHT has close tracking performance and faster computation compared to centralized algorithms.

Key words: multi-target tracking, probability multiple hypothesis tracking (PMHT), consensus, centralized state estimation, distributed state estimation

中图分类号:

TN953

姚思亦, 李万春, 高林, 张花国, 胡航玮. 基于分布式PMHT的多传感器多目标跟踪[J]. 系统工程与电子技术, 2024, 46(7): 2184-2190.

Siyi YAO, Wanchun LI, Lin GAO, Huaguo ZHANG, Hangwei HU. Multi-sensor multi-target tracking based on distributed PMHT[J]. Systems Engineering and Electronics, 2024, 46(7): 2184-2190.

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参考文献 19

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误差度量	杂波密度λ
误差度量	2×10^－4	3×10^－4	4×10^－4	5×10^－4	6×10^－4	7×10^－4	8×10^－4	9×10^－4	1×10^－3
${\bar E}$^sin	9.000 1	10.023 8	10.171 9	10.860 3	11.400 5	11.459 5	12.404 3	12.778 2	13.003 6
${\bar E}$^cen	0.607 9	0.629 4	0.653 3	0.646 4	0.658 9	0.675 8	0.690 1	0.698 5	0.701 7
${\bar E}$^dse	0.614 0	0.615 6	0.616 9	0.628 2	0.626 3	0.639 8	0.654 2	0.647 8	0.654 7

误差度量	迭代次数
误差度量	1	2	3	4	5	6	7	8	9	10
${\bar E}$^dse	0.698 3	0.607 6	0.576 0	0.563 9	0.559 2	0.557 4	0.556 7	0.556 4	0.556 3	0.556 2

PMHT算法	运行时间
集中式	9.109 4
分布式(L=5)	6.154 5
分布式(L=10)	6.310 2
分布式(L=15)	6.513 1

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基于分布式PMHT的多传感器多目标跟踪

Multi-sensor multi-target tracking based on distributed PMHT

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