A novel adaptive unscented Kalman filter for pico satellite attitude estimation

Unscented Kalman Filter (UKF) is a filtering algorithm which gives sufficiently good estimation results for estimation problems of nonlinear systems even in case of high nonlinearity. However, in case of system uncertainty UKF becomes to be inaccurate and diverges by time. In other words, if any change occurs in the process noise covariance, which is known as a priori, filter fails. This study, introduces a novel Adaptive Unscented Kalman Filter (AUKF) algorithm based on the correction of process noise covariance for the case of mismatches with the model. By the use of a newly adaptation scheme for the conventional UKF algorithm, change in the noise covariance is detected and corrected. Differently from the most of the existing adaptive UKF algorithms, covariance is not updated at each step; it has been only corrected when the fault occurs and that brings about a noteworthy reduction in the computational burden. Proposed algorithm is tested as a part of the attitude estimation algorithm of a pico satellite, a satellite type for which computational convenience is necessary because of the design limitations