Maximum correntropy unscented filter based on unbiased minimum-variance estimation for a class of nonlinear systems - Zhang, Yike; Niu, Ben; Song, Xinmin (Frontiers in Physics, 2024)
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 unknown input. When the nonlinear system is disturbed by non-Gaussian noise, the performance of UKF-UMV will seriously deteriorate. A maximum correntropy unscented filter based on unbiased minimumvariance (MCUF-UMV) estimation method is proposed on the basis of the UKF-UMV without the need for unknown input estimation, and uses the maximum correntropy criterion (MCC) and fixed-point iterative algorithm for state estimation. When the measurement noise of the nonlinear system is non-Gaussian noise, the algorithm performs well. Our proposed algorithm also does not require estimation of unknown input, and there is no prior knowledge available about the unknown input, nor make 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.