An Adaptive Robust Unscented Kalman Filter based Matching Algorithm for Underwater Gravity Aided Navigation

Gravity matching is the key technology of gravity aided inertial navigation. Traditional single point matching algorithm, SITAN algorithm, introduces large linearization error. The single point matching of UKF can reduce the linearization error and improve the matching accuracy effectively. However, under the situation of strong uncertainty of system process noise and the polluted measurement noise, UKF has poor performance. An adaptive robust Unscented Kalman Filter (ARUKF) based matching algorithm for gravity aided inertial navigation is proposed, which improves the robustness by introducing adaptive factor and robust function. Simulation results indicate that compared with algorithm based on standard UKF, the proposed algorithm can reduce the matching error more effectively, higher matching accuracy can be achieved ultimately.