A Robust GNSS Navigation Filter Based on Maximum Correntropy Criterion with Variational Bayesian for Adaptivity  

作　　者：Dah-Jing Jwo Yi Chang Ta-Shun Cho 

机构地区：[1]Department of Communications,Navigation and Control Engineering,National Taiwan Ocean University,Keelung,202301,Taiwan [2]Department of Electrical Engineering,National Taiwan Ocean University,Keelung,202301,Taiwan [3]Department of Business Administration,Asia University,500 Liufeng Road,Wufeng,Taichung,41354,Taiwan

出　　处：《Computer Modeling in Engineering & Sciences》2025年第3期2771-2789,共19页工程与科学中的计算机建模(英文)

基　　金：supported by the National Science and Technology Council,Taiwan under grants NSTC 111-2221-E-019-047 and NSTC 112-2221-E-019-030.

摘　　要：In this paper,an advanced satellite navigation filter design,referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter(VBMCEKF),is introduced to enhance robustness and adaptability in scenarios with non-Gaussian noise and heavy-tailed outliers.The proposed design modifies the extended Kalman filter(EKF)for the global navigation satellite system(GNSS),integrating the maximum correntropy criterion(MCC)and the variational Bayesian(VB)method.This adaptive algorithm effectively reduces non-line-of-sight(NLOS)reception contamination and improves estimation accuracy,particularly in time-varying GNSS measurements.Experimental results show that the proposed method significantly outperforms conventional approaches in estimation accuracy under heavy-tailed outliers and non-Gaussian noise.By combining MCC with VB approximation for real-time noise covariance estimation using fixed-point iteration,the VBMCEKF achieves superior filtering performance in challenging GNSS conditions.The method’s adaptability and precision make it ideal for improving satellite navigation performance in stochastic environments.

关 键 词：Maximum correntropy criterion variational Bayesian extended Kalman filter GNSS 

分 类 号：P228[天文地球—大地测量学与测量工程]