期刊
IEEE SENSORS JOURNAL
卷 23, 期 17, 页码 19524-19538出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2023.3283863
关键词
Bearings-only measurements; maneuvering target tracking; maximum correntropy; pseudolinear estimation
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. The proposed estimation method, including several algorithms, outperforms the traditional Kalman filter in non-Gaussian noise environments.
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.
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