Satellite autonomous navigation filtering algorithm based on improved strong tracking square-root UKF

For the satellite autonomous navigation system subjects to model uncertainties, external disturbances and noises, the unscented Kalman filter (UKF) method has low accuracy, poor tracking ability and poor robustness. An improved strong tracking square-root unscented Kalman filter (STSRUKF)-based autonomous navigation method is proposed. For the navigation purpose, star sensors and optical navigation cameras are used in this method, and the indirect measurement vector is transformed to observables through a transition equation. To avoid the problem that negative zero weights of sigma points and great calculation errors in square-root UKF (SRUKF) design for high-order systems, a modified square-root decomposition method is applied for the SRUKF design to improve the stability of the SRUKF. In addition, based on strong tracking filters (STF), multiple adaptive fading factors in adjustment covariance matrix are adopted so that the STSRUKF has better tracking ability, better robustness against model uncertainties and better estimation accuracy. Finally, the STSRUKF-based method is applied to the satellite autonomous navigation systems, and simulation results are provided to verify the effectiveness and practicability of the proposed approach.