Extended target tracking algorithm based on MM-GGIW-PMBM filter

被引：0
|
作者：
Wu S. [1 ,2 ]
Zhou Y. [1 ]
Xie Y. [1 ]
Cai R. [1 ]
Fan X. [1 ]
机构：
[1] School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin
[2] Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2022年 / 48卷 / 12期
基金：
中国国家自然科学基金;
关键词：
extended target tracking; Gamma Gaussian inverse Wishart; interactive multiple models; Poisson multi-Bernoulli mixture filtering; strong tracking filter;
D O I：
10.13700/j.bh.1001-5965.2021.0162
中图分类号：
学科分类号：
摘要：
To address the problem of multiple maneuvering extended target tracking, this paper introduces the concept of interactive multiple models into the Poisson multi-Bernoulli mixture filtering (PMBM) algorithm, and proposes a multi-model algorithm with Gamma Gaussian inverse Wishart and PMBM (MM-GGIW-PMBM) . Firstly, the algorithm integrates multiple motion models and realizes the hybrid estimation and prediction of the extended state of the maneuvering target and the centroid state through the interaction of the models. Secondly, the covariance matrix in the predicted GGIW components is modified by introducing the fading factor into the strong tracking filter (STF) to prevent the tracking model mismatch. Finally, the target shape is expanded in the PMBM update stage based on the completion of centroid estimation, and the likelihood function is used to update the model probability. The simulation shows that the proposed algorithm can effectively estimate the number and state of multiple maneuvering extended targets. © 2022 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
引用
收藏
页码：2356 / 2364
页数：8
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