Adaptive maximum correntropy based robust CKF with variational Bayesian for covariance estimation

Abstract

To address the interference of outliers on the estimation of state and measurement noise covariance matrix, an adaptive maximum correntropy cubature Kalman filter with variational Bayesian approximation over a sliding window is proposed. The multiple kernel size is adjusted for different noise within a reasonable range based on the squared Mahalanobis distance of innovation, which overcomes the excessive convergence problem in the adjustment process. The correntropy matrix is established using the adaptive multiple kernel size to achieve measurement-specific outliers processing. Then the measurement noise covariance matrix is updated as inverse Wishart distribution exploiting the posterior smoothing-based variational Bayesian approximations with correntropy matrix, suppressing the disturbance of measurement outliers to the modification of the measurement noise covariance matrix. Finally, the target tracking simulation and cooperative positioning experiment demonstrate that the proposed method can effectively achieve the robust state estimation with accurate modification of MNCM in the presence of outliers.