GLMB extended target tracking based on one-step data association

被引：0
|
作者：
Li C. [1 ]
Li Y. [1 ]
Ji H. [1 ]
Shi R. [1 ]
机构：
[1] School of Electronic Engineering, Xidian University, Xi'an
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2020年 / 47卷 / 05期
关键词：
Data association; Extended target tracking; Generalized labeled multi-bernoulli filter; Multiplicative noise model; Second-order extended kalman filtering;
D O I：
10.19665/j.issn1001-2400.2020.05.018
中图分类号：
学科分类号：
摘要：
Due to the inseparability of measurements in neighborhood scenarios, the tracking performance of the traditional extended target tracking algorithm would degrade. In this paper, a new extended target tracking algorithm based on one step data association is proposed to solve the problem. First, the algorithm models the target with a multiplicative noise model. And then, the one step data association method in the Joint Probabilistic Data Association (JPDA) theory is combined with a Generalized Labeled Multi-Bernoulli (GLMB) filter. Simulation results show that the algorithm can track the target in cross and neighborhood scenarios effectively and that it is superior to the traditional extended target tracking algorithms based on measurement partition in estimation accuracy. © 2020, The Editorial Board of Journal of Xidian University. All right reserved.
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页码：137 / 143
页数：6
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