An Integrated Navigation Algorithm for Underwater Vehicles Improved by a Variational Bayesian and Minimum Mixed Error Entropy Unscented Kalman Filter

Abstract

In complex marine environments, autonomous underwater vehicles (AUVs) rely on robust navigation and positioning. Traditional algorithms face challenges from sensor outliers and non-Gaussian noise, leading to significant prediction errors and filter divergence. Outliers in sensor observations also impact positioning accuracy. The original unscented Kalman filter (UKF) based on the minimum mean square error (MMSE) criterion suffers from performance degradation under these conditions. This paper enhances the minimum error entropy unscented Kalman filter algorithm using variational Bayesian (VB) methods and mixed entropy functions. By implementing minimum error entropy (MEE) and mixed kernel functions in the UKF, the algorithm’s robustness under complex underwater conditions is improved. The VB method adaptively fits the measurement noise covariance, enhancing adaptability to marine environments. Simulations and sea trials validate the proposed algorithm’s performance, showing significant improvements in navigation accuracy and root mean square error (RMSE). In environments with complex noise, our algorithm improves the overall navigation accuracy by at least 10% over other existing algorithms. This demonstrates the high accuracy and robustness of the algorithm.