Improved Maximum Correntropy Unscented Kalman Filter for Spacecraft Attitude Estimation

In this article, an improved maximum correntropy unscented Kalman filter (IMCUKF) is proposed for the problem of mutation and non-Gaussian noise in the spacecraft attitude estimation system. The IMCUKF proposed in this paper transforms the above problem into a nonlinear regression problem, which is achieved by establishing a robust filter based on the maximum correntropy criterion and adaptively adjusting the prior covariance using an adaptive fading factor. In particular, the IMCUKF uses the Cauchy kernel function instead of the Gaussian kernel function to solve the problem that the maximum correntropy Kalman filter is easy to collapse due to the emergence of singular matrices. In addition, this paper applies the additive quaternion property to derive the Kalman filter gain to satisfy the constraint of quaternion normalization. Simulation results demonstrate the superiority of the proposed algorithm as compared with the existing filtering algorithm with the measurement outliers, state mutations, and non-Gaussian noises.