Soft-Gating Mixture Robust Kalman Filter for SINS/DVL Integrated Navigation Under DVL Outlier Interference
Year: 2026
Type: article
Abstract: Aiming at the problem that complex underwater environments induce outliers in Doppler Velocity Log (DVL) measurements, which degrade the navigation accuracy of the Strapdown Inertial Navigation System (SINS)/DVL integrated system, this paper proposes a soft-gating Gaussian–Student’s t mixture robust Kalman filter (MRKF). Firstly, the measurement noise is modeled as a mixture of Gaussian and Student’s t distributions to adapt to normal stationary noise and abrupt outliers, respectively. Secondly, a logistic soft-gating weight is constructed based on the innovation Mahalanobis distance to adaptively balance the output contributions of the standard Kalman Filter (KF) and the variational Bayesian Student’s t filter. Finally, moment matching is adopted to realize the weighted fusion of two-branch posterior distributions, and an equivalent Gaussian posterior estimation is obtained. Simulation results under the considered SINS/DVL integrated navigation scenarios show that the proposed MRKF maintains estimation accuracy close to the standard KF under nominal Gaussian measurement noise. In the designed DVL outlier-injection scenario, the proposed MRKF achieves a position RMSE of 53.39m, compared with 878.75m, 58.84m, and 56.49m for the nominal KF, Huber KF (HKF), and Student’s-t variational Bayesian KF (STVBKF), respectively. These results indicate that the proposed MRKF can improve robustness against DVL outliers while maintaining competitive estimation accuracy under the simulated conditions.
DOI: 10.3390/jmse14131165
Authors: Li Luo, Luyao Zhang, Congyi Yang, Tao Liu
Institution: Chengdu University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G1128017177, G190675921, G2224489978
OpenAlex ID: W7165795594