Adaptive Multikernel Size-Based Maximum Correntropy Cubature Kalman Filter for the Robust State Estimation

Abstract

The performance of the maximum correntropy criterion filter is affected by the kernel size, while the present kernel size adaptive methods are prone to excessive convergence. To achieve the adaptive adjustment for the kernel size without excessive convergence problems, an adaptive multikernel size-based maximum correntropy cubature Kalman filter (CKF) is proposed. The adaptive factor for each measurement element is constructed, and the kernel size is adjusted within a reasonable range based on the adaptive factor. Then the correntropy matrix with the adaptive multikernel size is established to achieve measurement-specific outliers processing in state estimation. The target tracking simulation and the cooperative positioning experiment are conducted to verify the proposed method. The results demonstrate that the proposed adaptive multikernel size-based maximum correntropy CKF (AMCCKF) can effectively optimize the kernel size for different noises and is more convenient for selecting tuning parameters, thus effectively achieving robust state estimation against outliers while ensuring filtering accuracy.