Open Access

ARTICLE

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

Dah-Jing Jwo^1,2,*, Yi Chang², Ta-Shun Cho³

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

* Corresponding Author: Dah-Jing Jwo. Email:

(This article belongs to the Special Issue: Scientific Computing and Its Application to Engineering Problems)

Computer Modeling in Engineering & Sciences 2025, 142(3), 2771-2789. https://doi.org/10.32604/cmes.2025.057825

Received 28 August 2024; Accepted 14 January 2025; Issue published 03 March 2025

Abstract

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.

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Keywords

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