A New Robust Kalman Filter for SINS/DVL Integrated Navigation System

A new robust strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation algorithm are proposed in this paper with a focus on suppressing the process uncertainty and measurement outliers induced by severe manoeuvering. In the proposed algorithm, the one-step predicted probability density function is modeled as Student's t-distribution to deal with the heavy-tailed process noise, and hierarchical Gaussian state-space model for SINS/DVL integrated navigation algorithm is constructed. To detect and eliminate the measurement outliers, each measurement is marked by a binary indicator variable modeled as a beta-Bernoulli distribution. The variational Bayesian approach is used to jointly estimate state vector, auxiliary random variable, scale matrix, Bernoulli variable, and beta variable. The experimental results illustrate that the proposed algorithm has better robustness and navigation accuracy to deal with process uncertainty and measurement outliers than existing state-of-the-art algorithms.