Maximum correntropy unscented filter based on unbiased minimum-variance estimation for a class of nonlinear systems

被引:0
作者
Zhang, Yike [1 ]
Niu, Ben [1 ]
Song, Xinmin [1 ]
机构
[1] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China
来源
FRONTIERS IN PHYSICS | 2024年 / 12卷
基金
中国国家自然科学基金;
关键词
maximum correntropy criterion; unbiased minimum-variance; unscented Kalman filter; unknown input; state estimation; KALMAN FILTER; STATE ESTIMATION; INPUTS;
D O I
10.3389/fphy.2024.1347843
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Introduction: The unscented Kalman filter based on unbiased minimum-variance (UKF-UMV) estimation is usually used to handle the state estimation problem of nonlinear systems with an unknown input. When the nonlinear system is disturbed by non-Gaussian noise, the performance of UKF-UMV will seriously deteriorate.Methods: A maximum correntropy unscented filter based on the unbiased minimum variance (MCUF-UMV) estimation method is proposed on the basis of the UKF-UMV without the need for estimation of an unknown input and uses the maximum correntropy criterion (MCC) and fixed-point iterative algorithm for state estimation.Results: When the measurement noise of the nonlinear system is non-Gaussian noise, the algorithm performs well.Discussion: Our proposed algorithm also does not require estimation of an unknown input, and there is no prior knowledge available about the unknown input or any prior assumptions. The unknown input can be any signal. Finally, a simulation example is used to demonstrate the effectiveness and reliability of the algorithm.
引用
收藏
页数:10
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