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

被引：0
|
作者：
Gong Y. [1 ]
Cui C. [1 ]
机构：
[1] Institute of Electronic Countermeasure, National University of Defense Technology, Hefei
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 01期
关键词：
False alarm detection; Missing alarm refinement; Probability hypothesis density (PHD); Spatially close target; Weight rearrangement;
D O I：
10.12305/j.issn.1001-506X.2022.01.11
中图分类号：
O211 [概率论（几率论、或然率论）];
学科分类号：
摘要：
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. © 2022, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：76 / 85
页数：9
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