Correntropy weighted robust Kalman filter for integrated navigation system with time varying noise covariance

Abstract To handle the influence of historical data on state estimation, this study proposes a correntropy-weighted robust Kalman filter (CWRKF). The proposed method investigates the weight using the correntropy based on the current residual, thus suppressing the impact of historical data on system state estimation. In addition, an adaptive kernel bandwidth is defined for correntropy to avoid excessive use of the current residual and accuracy degradation. Further, the correntropy-weighted system noise covariance estimation is obtained and embedded into the Kalman filter. The proposed CWRKF is verified by simulations, and the results demonstrate the superiority of the proposed CWRKF in state estimation under time-varying system noise covariance conditions over the existing approaches.