Particle Probability Hypothesis Density Filter Based on Stochastic Perturbation Re-sampling

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

doi:10.11999/JEIT160114

Volume 38 Issue 11

Dec. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(11): 2819-2825

XU Cong’an, HE You, XIA Shutao, CHENG Juntu, DONG Yunlong. Particle Probability Hypothesis Density Filter Based on Stochastic Perturbation Re-sampling[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2819-2825. doi: 10.11999/JEIT160114

Citation:

XU Cong’an, HE You, XIA Shutao, CHENG Juntu, DONG Yunlong. Particle Probability Hypothesis Density Filter Based on Stochastic Perturbation Re-sampling[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2819-2825. doi: 10.11999/JEIT160114

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Particle Probability Hypothesis Density Filter Based on Stochastic Perturbation Re-sampling

doi: 10.11999/JEIT160114

1.
2.
(Unit. 91213 of PLA, Yantai 264000, China)

Funds:

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

Received Date: 2016-01-26
Rev Recd Date: 2016-07-08
Publish Date: 2016-11-19

Abstract

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.
- Multi-Target Tracking (MTT),
- Probability Hypothesis Density (PHD),
- Particle Filter (PF),
- Stochastic perturbation re-sampling

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References(30)

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