Robust State Estimation Using the Maximum Correntropy Cubature Kalman Filter with Adaptive Cauchy-Kernel Size

Abstract

The maximum correntropy criterion (MCC), as an effective method for dealing with anomalous measurement noise, is widely applied in the design of filters. However, its performance largely depends on the proper setting of the kernel bandwidth, and currently, there is no efficient adaptive kernel adjustment mechanism. To deal with this issue, a new adaptive Cauchy-kernel maximum correntropy cubature Kalman filter (ACKMC-CKF) is proposed. This algorithm constructs adaptive factors for each dimension of the measurement system and establishes an entropy matrix with adaptive kernel sizes, enabling targeted handling of specific anomalies. Through simulation experiments in target tracking, the performance of the proposed algorithm was comprehensively validated. The results show that the ACKMC-CKF, through its flexible kernel adaptive mechanism, can effectively handle various types of anomalies. Not only does the algorithm demonstrate excellent reliability, but it also has low sensitivity to parameter settings, making it more broadly applicable in a variety of practical application scenarios.