Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets

被引：4
|
作者：
Zhang H. [1 ,2 ]
Ge H. [2 ]
Yang J. [2 ]
机构：
[1] School of Electronic and Electrical Engineering, Shangqiu Normal University
[2] School of Internet of Things Engineering, Jiangnan University, Jiangnan
来源：
International Journal of Electronics and Telecommunications | 2017年 / 63卷 / 03期
基金：
中国国家自然科学基金;
关键词：
closely spaced targets; Gaussian mixture PHD; probability hypothesis density filter; random finite set; weight redistribution;
D O I：
10.1515/eletel-2017-0033
中图分类号：
学科分类号：
摘要：
Probability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm. © 2017 by Hongwei Ge.
引用
收藏
页码：247 / 254
页数：7
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