基于随机摄动再采样的粒子概率假设密度滤波器

doi:10.11999/JEIT160114

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Abstract

As a typical implementation of the Probability Hypothesis Density (PHD) filter, Particle PHD (P-PHD) is suitable for highly nonlinear systems and widely used in Multi-Target Tracking (MTT). However, the resampling in P-PHD filter, recommended to avoid particle degeneracy, introduces the problem of diversity loss among the particles, namely particle impoverishment problem. To solve the problem and improve the performance of the P-PHD filter, a novel filter based on stochastic perturbation re-sampling is proposed. First, a comprehensive analysis on the particle impoverishment problem of P-PHD filter is presented. Then for the purpose of keeping the particle diversity, a new stochastic perturbation re-sampling algorithm is developed, which generates new particles according to the position and duplicating times of the original particles, and removes some excessive copied particles. Finally, the re-sampling algorithm is integrated into the P-PHD filter framework and a Stochastic Perturbation Particle PHD (SPP-PHD) filter is proposed. Numerical examples show that the proposed filter can overcome the particle impoverishment problem and improve the estimation performance on the premise of not significantly improving the simulation time.

Key words： Multi-Target Tracking (MTT) Probability Hypothesis Density (PHD) Particle Filter (PF) Stochastic perturbation re-sampling

Received: 26 January 2016 Published: 30 September 2016

PACS:

TN391

Fund:

The National Natural Science Foundation of China (61471383, 61304103)

Corresponding Authors: XU Cong’an E-mail: xcatougao@163.com

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	XU Cong’an
	HE You
	XIA Shutao
	CHENG Juntu
	DONG Yunlong

Cite this article:

XU Cong’an,HE You,XIA Shutao等. Particle Probability Hypothesis Density Filter Based on Stochastic Perturbation Re-sampling[J]. JEIT, 2016, 38(11): 2819-2825.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT160114 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2819

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