Algorithm Research & Explore
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1652-1656

Student’s t particle implementations of probability hypothesis density filters

Hong Lei1
Chen Shuxin1
Wu Hao1
Xu Han1
Yue Longhua2
1. Institute of Information & Navigation, Air Force Engineering University, Xi'an 710077, China
2. Unit 93658 of PLA, Beijing 100144, China

Abstract

For the nonlinear multi-target tracking problem, the heavy-tailed process and measurement noises can reduce the performance of the Gaussian mixture particle probability hypothesis density(GMP-PHD) filter severely. To solve this problem, this paper proposed a new student's t mixture particle probability hypothesis density filter(STMP-PHD). The method used a student's t model to approximate the process noise and the measurement noise, and used a student's t mixture model to approximate the intensity of the multi-target. The algorithm made the Monte Carlo method to calculate the student's t integral, and establish the student's t mixture closed recursive framework. The simulation results confirmed that the filter can effectively overcome the negative effects of the heavy-tailed process noise and the measurement noise, and maintain the high tracking precision.

Foundation Support

国家自然科学基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.11.0905
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 6
Section: Algorithm Research & Explore
Pages: 1652-1656
Serial Number: 1001-3695(2020)06-009-1652-05

Publish History

[2020-06-05] Printed Article

Cite This Article

洪磊, 陈树新, 吴昊, 等. 一种学生t混合粒子实现的概率假设密度滤波器 [J]. 计算机应用研究, 2020, 37 (6): 1652-1656. (Hong Lei, Chen Shuxin, Wu Hao, et al. Student’s t particle implementations of probability hypothesis density filters [J]. Application Research of Computers, 2020, 37 (6): 1652-1656. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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