Fault tolerant attitude estimation for pico satellites using robust adaptive UKF

Unscented Kalman Filter (UKF) is a filtering algorithm which gives sufficiently good estimation results for estimation problems of nonlinear systems even when high nonlinearity is in question. However, in case of system uncertainty or measurements malfunctions the UKF becomes to be inaccurate and diverges by time. This study, introduces a fault tolerant attitude estimation algorithm for pico satellites. The algorithm uses a Robust Adaptive Unscented Kalman Filter (RAUKF) which performs correction for process noise covariance (Q-adaptation) or measurement noise covariance (R-adaptation) depending on the type of the fault. By the use of a newly proposed adaptation scheme for the conventional UKF algorithm, the fault is detected; isolated and the essential adaptation procedure is followed in accordance with the fault type. The 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.