基于GM-PHD滤波的空间邻近多目标跟踪算法

doi:10.12305/j.issn.1001-506X.2022.01.11

Abstract

Abstract:

Considering the problem of wrong estimate, missing alarm and false alarm when the Gaussian mixture probability hypothesis density (GM-PHD) filter is used to track targets which are spatially close, an improved algorithm is proposed. Firstly, by arranging the weights of Gaussian components assigned to each target, a weight rearrangement scheme is proposed to improve the tracking accuracy of the GM-PHD filter when targets are spatially close. Then, based on continuous property of the target trajectory, the missed target at the current time is refined by the predicted value at the last time to reduce the missing alarm. Finally, the estimated targets are classified by making full use of the multi-frame estimated target states, and the false alarm is detected and deleted. Simulation results demonstrate that the improved algorithm has a better tracking performance compared with the existing algorithms.

Key words: probability hypothesis density (PHD), spatially close target, weight rearrangement, missing alarm refinement, false alarm detection

CLC Number:

TP391

Yang GONG, Chen CUI. Multi-target tracking algorithm based on GM-PHD filter for spatially close targets[J]. Systems Engineering and Electronics, 2022, 44(1): 76-85.

Figures/Tables 7

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References 27

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滤波器	场景1	场景2	场景3
GM-PHD	1.327 6	1.077 1	0.468 3
CPGM-PHD	1.389 5	1.242 0	0.474 9
IUC-PHD	1.919 6	1.596 9	0.703 6
IM-GM-PHD	1.401 1	1.252 0	0.501 0

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Multi-target tracking algorithm based on GM-PHD filter for spatially close targets

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