Application of Adaptive Unscented Kalman Filter in Underwater Integrated Navigation System
[Objective]To solve the problem of degraded fusion filtering performance in underwater integrated navigation systems when the prior noise distribution does not match the actual noise distribution,and improve the navigation accuracy of autonomous underwater vehicles.[Method]This paper proposed an improved robust adaptive UKF(AUKF)filtering algorithm by introducing adaptive factors to improve the UKF filtering algorithm.The combination of velocity term and state variable in the system state equation was reconstructed to solve the inconsistency problem of system variance.The effectiveness of the proposed algorithm wasverified by ship experiment and semi-physical simulation.[Result and Conclusion]The experimental results show that compared with UKF algorithm,the latitude RMSE,longitude RMSE and height RMSE of AUKF algorithm are reduced by 27%,27%and 25%in terms of average position estimation deviation.Therefore,the improved robust AUKF can effectively suppress the filter divergence in the face of the disturbance of the system,andimprove the navigation accuracy of autonomous underwater vehicle(AUV)in underwater environment.