基于航迹概率假设密度的多传感器多目标跟踪

doi:10.12305/j.issn.1001-506X.2024.02.17

Abstract

Abstract:

Aiming at the problems that the distributed multi-sensor multi-target tracking (DMMT) method based on probability hypothesis density (PHD) cannot form a track, the computational complexity is high, and miss-detections. In this paper, a DMMT method is developed with trajectory PHD posterior estimation. Firstly, the similarity measure matrix between the estimated trajectories of each node is constructed, and the optimal trajectory matching is achieved by using the Hungarian algorithm. Secondly, the covariance intersection rule is used to achieve parallel fusion for the associated trajectories. Finally, a robust DMMT method is derived based on probabilistic generative functionals. Simulation experiments verify the advantages of the proposed algorithm in terms of multi-target state estimation accuracy, computational efficiency and real-timeliness.

Key words: trajectory probability hypothesis density, optimal trajectories matching, generalized covariance intersection, probabilistic generative functionals

CLC Number:

TN957

Zhiwei WANG, Yongxiang LIU, Wei YANG, Zhejun LU. Multi-sensor multi-target tracking with trajectory probability hypothesis density[J]. Systems Engineering and Electronics, 2024, 46(2): 526-533.

Figures/Tables 4

References 39

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算法	GOSPA误差	位置误差	漏检误差	错误误差
PHD(1)^[10]	8.73	2.24	7.10	4.09
PHD(2)^[10]	8.66	2.21	7.12	3.96
MeMBer(1)^[11]	6.71	2.09	5.36	2.99
MeMBer(2)^[11]	6.70	2.08	5.41	2.94
TPHD(1)^[14]	7.58	2.01	5.83	3.86
TPHD(2)^[14]	7.60	2.08	5.80	3.90
PHD-GCI^[24]	8.10	2.23	6.16	4.23
PHD-WAA^[20]	7.07	2.21	4.13	4.82
B2B-AA-Flooding^[34]	5.27	2.09	3.77	2.49
TPHD-TA-GCI	4.59	1.62	2.80	2.27

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Multi-sensor multi-target tracking with trajectory probability hypothesis density

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