Improved Maximum Correntropy Cubature Kalman Filter for Cooperative Localization

Abstract

In this paper, an improved maximum correntropy cubature kalman filter(IMCCKF) is proposed to address the measurement outliers in cooperative localization(CL) of autonomous underwater vehicles (AUVs). The estimated performance of the maximum correntropy cubature kalman filter(MCCKF) algorithm is affected by the kernel bandwidth(KB). The selection value of the KB cannot be determined only by experience in practical CL of AUVs, which will greatly reduce the practical application value of the MCCKF algorithm. The adaptive factor is constructed by comparing the trace size of innovation matrix and the trace size of quantity prediction error matrix, and the KB in the MCCKF is adjusted online by the adaptive factor. Finally, the validity of the proposed IMCCKF method is verified by the lake test data. The experimental results show that the proposed method has the ability to adjust the KB in real time and quickly obtain the optimal value of the KB, and the IMCCKF algorithm can effectively improve the positioning performance of CL system with measurement outliers.