A Student’s t Poisson multi-Bernoulli mixture filter in the presence of heavy-tailed noise

被引：0

作者：

Zhao, Zi-Wen ^{[1
]}

Chen, Hui ^{[1
]}

Lian, Feng ^{[2
]}

Zhang, Guang-Hua ^{[2
]}

机构：

[1] School of Electrical and Information Engineering, Lanzhou University of Technology, Gansu, Lanzhou

[2] School of Automation Science and Engineering, Xi’an Jiaotong University, Shaanxi, Xi’an

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 09期

基金：

中国国家自然科学基金;

关键词：

heavy-tail noise; multi-target tracking; Poisson multi-Bernoulli mixture; random finite set; Student’s t mixture;

D O I：

10.7641/CTA.2023.20625

中图分类号：

学科分类号：

摘要：

Aiming at the complex uncertainty multi-target tracking where both the motion process and observation process are disturbed by anomalous noise, this paper innovatively proposes a Student’s t mixture Poisson multi-Bernoulli mixture filter. First, the anomalous noise characteristics of the wide-area distribution are directly modeled as the Student’s t distribution. Subsequently, the probability density parameters of the Poisson point process (PPP) and the multi-Bernoulli mixture (MBM) of the Poisson multi-Bernoulli mixture filter are reasonably approximated by the Student’s t mixture form. Moreover, based on the Student’s t mixture model which approximates the multi-target probability density, the Student’s t mixture conjugate prior form of Poisson multi-Bernoulli mixture filter is derived in detail and a closed-form recursive framework of Student’s t mixture Poisson multi-Bernoulli mixture is established. Finally, the effectiveness of the proposed filtering algorithm is verified by complex multi-target tracking simulation experiments under the joint interference of process noise and measurement noise with significant trailing distribution characteristics. © 2024 South China University of Technology. All rights reserved.

引用

页码：1598 / 1609

页数：11

共 44 条

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