A New Robust Adaptive Filter Aided by Machine Learning Method for SINS/DVL Integrated Navigation System

Abstract

As an important means of underwater navigation and positioning, the accuracy of SINS/DVL integrated navigation system greatly affects the efficiency of underwater work. Considering the complexity and change of the underwater environment, it is necessary to enhance the robustness and adaptability of the SINS/DVL integrated navigation system. Therefore, this paper proposes a new adaptive filter based on support vector regression. The method abandons the elimination of outliers generated by Doppler Velocity Logger (DVL) in the measurement process from the inside of the filter in the form of probability density function modeling. Instead, outliers are eliminated from the perspective of external sensors, which effectively improves the robustness of the filter. At the same time, a new Variational Bayesian (VB) strategy is adopted to reduce the influence of inaccurate process noise and measurement noise, and improve the adaptiveness of the filter. Their advantages complement each other, effectively improve the stability of filter. Simulation and ship-borne tests are carried out. The test results show that the method proposed in this paper has higher navigation accuracy.

Keywords

• SINS/DVL integrated system
• support vector regression
• Variational Bayesian adaptive Kalman filter
• robust and adaptive