Track fusion for airborne radar network using message passing

被引：0

作者：

Bai X.-L. ^{[1
]}

Pan Q. ^{[1
]}

Ma E.-C. ^{[1
]}

Hao Y.-H. ^{[1
]}

Yun T. ^{[1
]}

机构：

[1] Key Laboratory of Information Fusion Technology, Ministry of Education, School of Automation, Northwestern Polytechnical University, Shaanxi, Xi’an

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 07期

基金：

中国国家自然科学基金;

关键词：

belief propagation; mean field approximation; message passing; probabilistic graphic model; track fusion;

D O I：

10.7641/CTA.2024.30611

中图分类号：

学科分类号：

摘要：

Airborne radar network track fusion requires three sub-problems: target tracking, data association and track management, which are coupled with each other and therefore require a joint solution. In this paper, we propose a track fusion method for airborne radar network using massage passing, which jointly solves the three sub-problems of target tracking, data association and track management. Firstly, the joint probability density function of airborne radar network track fusion is established, and its factorised form is converted into a factor graph. The statistical model of the target kinematic state is a conjugate exponential, and the mean-field approximation is used to obtain a simple message passing. The data association contains one-to-one constraints, and belief propagation is used. The target visibility state is also approximated by belief propagation to obtain better approximation results. Finally, the posterior probability densities can be approximated by a closed-loop iterative framework to effectively deal with the coupling problem between target tracking, data association and track management. Simulation results show that the proposed algorithm outperforms the multiple hypothesis tracking algorithm and the joint probability density association algorithm. © 2024 South China University of Technology. All rights reserved.

引用

页码：1235 / 1245

页数：10

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