Sensors, Vol. 18, Pages 1960: Adaptive Maximum Correntropy Gaussian Filter Based on Variational Bayes

Sensors, Vol. 18, Pages 1960: Adaptive Maximum Correntropy Gaussian Filter Based on Variational Bayes
Sensors doi: 10.3390/s18061960
Authors:
Guoqing Wang
Zhongxing Gao
Yonggang Zhang
Bin Ma

In this paper, we investigate the state estimation of systems with unknown covariance non-Gaussian measurement noise. A novel improved Gaussian filter (GF) is proposed, where the maximum correntropy criterion (MCC) is used to suppress the pollution of non-Gaussian measurement noise and its covariance is online estimated through the variational Bayes (VB) approximation. MCC and VB are integrated through the fixed-point iteration to modify the estimated measurement noise covariance. As a general framework, the proposed algorithm is applicable to both linear and nonlinear systems with different rules being used to calculate the Gaussian integrals. Experimental results show that the proposed algorithm has better estimation accuracy than related robust and adaptive algorithms through a target tracking simulation example and the field test of an INS/DVL integrated navigation system.

Source: Sensors - June 17, 2018 Category: Biotechnology Authors: Guoqing Wang Zhongxing Gao Yonggang Zhang Bin Ma Tags: Article Source Type: research