An Improved Robust Interacting Multiple Model Algorithm for Underwater Acoustic Navigation

In underwater acoustic long baseline navigation, the motion state of underwater vehicle is changeable and there are abnormal observations, which reduces the navigation accuracy of traditional filtering algorithm and cannot meet the demand of high precision underwater navigation. To solve this problem, this paper proposes an improved robust interacting multiple model algorithm. Firstly, the robust estimation of each model is carried out, and the equivalent variance and model probability of each model are obtained. The weight of equivalent variance is determined according to the model probability, and the mixed equivalent variance is obtained by weighted summation of equivalent variance. The mixed equivalent variance is used to replace the measurement noise variance in each model for state estimation, and the interacting robust estimation is realized. Then the model probability is updated, and the power function with faster growth rate is used to replace the matching model probability to realize the model probability correction. Finally, the final state estimation value and its covariance matrix are obtained by weighted summation of the results of the interacting robust estimation of each model according to the revised model probability, so as to improve the navigation accuracy and stability of underwater vehicles. By processing simulation and measured data, the results show that compared with Kalman filter, interactive multi-model and robust interactive multi-model, in the simulation data, the navigation error of the proposed method is reduced by 64.18%, 26.35% and 10.61%, respectively; in the measured data, the navigation accuracy of this method is improved by 31.79%, 14.76% and 14.93% respectively, and the navigation accuracy reaches 3.7687 m in 3 km × 3 km. Compared with the traditional filtering algorithm, the improved robust interacting multiple model algorithm can significantly improve the navigation accuracy and stability of underwater vehicles.