Adaptive robust maximum correntropy cubature Kalman filter for spacecraft attitude estimation

Abstract

In this paper, an adaptive robust maximum correntropy cubature Kalman filter (AMCCKF) for spacecraft attitude estimation is proposed. The main aim is to enable the algorithm to resist the influence of noise non-Gaussian and insufficient statistical information on process noise in the system. Based on the maximum correntropy criterion in this study, the robust filtering algorithm is derived by establishing the regression model. In addition, the Cauchy kernel function is used to replace the Gaussian kernel function in the cost function to prevent a singular matrix from appearing in algorithm operation. Furthermore, the process noise is corrected by an adaptive fading factor. The quaternion normalization problem is solved by constructing a minimum constraint cost function using additive quaternion property. The simulation results show that the proposed AMCCKF algorithm has better estimation accuracy and robustness under the conditions of non-Gaussian noise and insufficient statistical information of process noise.