Attitude Estimation of SINS on Underwater Dynamic Base With Variational Bayesian Robust Adaptive Kalman Filter

Abstract

Kalman filter (KF), as an effective method to solve the attitude estimation problem in the initial alignment process of strapdown inertial navigation system (SINS), has been widely used in engineering practice. Unfortunately, in the complex underwater environment, the measurement is easily contaminated by outliers, and the measurement noise covariance is unknown or time-varying, which will degrade the performance of KF severely. To solve this problem, this paper proposes an improved KF with both robustness and adaptivity (namely VBRAKF), and applies it to the in-motion attitude estimation for Doppler velocity log (DVL) aided SINS. In the VBRAKF, the robustness is achieved by suppressing the outliers based on Mahalanobis distance (MD) of the innovation, and the adaptivity is achieved by estimating the uncertain measurement noise covariance based on variational Bayesian (VB) approximation simultaneously. The SINS/DVL shipboard tests were carried out when the outputs of DVL are contaminated by outliers or Gaussian mixture distribution noise, respectively. The tests results demonstrate the superiority of the proposed method over the traditional methods.