Robust Interactive Multimodel INS/DVL Intergrated Navigation System With Adaptive Model Set

Abstract

A Robust Interactive Multiple Model (RIMM) algorithm with the adaptive model set is proposed to improve the performance of Inertial Navigation System (INS) and Doppler Velocity Logs (DVL) integrated navigation system under complex measurement environment with undesirable heavy-tailed non-Gaussian noise. Specifically, an improved Huber kernel function is applied to the robust error state Kalman filter (ESKF) for its benefit of better resisting larger measurement outliers. In addition, a flexible adaptive model set update strategy is proposed where the model set is determined by the current and historical measurement information stored in the sliding window. And for model set update, the main model is no longer fixed, it will be determined based on the probability weights corresponding to each model. This innovative RIMM algorithm is compared with error state Kalman filter, Interactive Multiple Model (IMM) and Hybrid Interactive Multiple Model (HIMM) through simulations, AUV lake trial and semi-physical simulations. Experimental results show that our proposed algorithm has outstanding accuracy and robustness under heavy-tailed non-Gaussian noise environment.