A Pseudolinear Maximum Correntropy Kalman Filter Framework for Bearings-Only Target Tracking

被引：15

作者：

Zhong, Shan ^{[1
]}

Peng, Bei ^{[1
]}

Ouyang, Lingqiang ^{[2
]}

Yang, Xinyue ^{[2
]}

Zhang, Hongyu ^{[2
]}

Wang, Gang ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China

来源：

IEEE SENSORS JOURNAL | 2023年 / 23卷 / 17期

基金：

中国国家自然科学基金;

关键词：

Bearings-only measurements; maneuvering target tracking; maximum correntropy; pseudolinear estimation; RADAR; LOCALIZATION; PERFORMANCE; ALGORITHM; MOTION;

D O I：

10.1109/JSEN.2023.3283863

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This article presents a framework for a pseudolinear Kalman filter (PLKF) based on the maximum correntropy criterion for the bearings-only target tracking problem in non-Gaussian environments. We first derive a pseudolinear maximum correntropy Kalman filter (PMCKF). To solve the offset problem, bias compensation is merged into PMCKF to realize bias-compensated PMCKF (BC-PMCKF). In the real scenario, the speed variation of the target is continuous during motion. Based on this premise, we implement the speed-constrained PMCKF (SC-PMCKF) algorithm in this framework, which suppresses the effect of impulsive noise on velocity estimation well. Finally, a posterior Cramer-Rao lower bound (PCRLB) under non-Gaussian noises is derived for the framework. Simulations and physical experiments show that the proposed estimation method is better than the traditional Kalman filter in non-Gaussian noise environments.

引用

页码：19524 / 19538

页数：15

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