Adaptive Particle Filtering With Variational Bayesian and Its Application for INS/GPS Integrated Navigation
Yulu Zhong, Xiyuan Chen, Yunchuan Zhou, Junwei Wang
Abstract
This article considers the unknown measurement noise covariance problem in the nonlinear situation of a navigation system. Aiming at the contaminated global position system (GPS) signals and the outlier environment, there are many variational Bayesian (VB)-based Gaussian approximation methods in the integrated navigation system (INS). However, the integrated navigation is nonlinear, especially for the inaccurate initial state provided by the initial alignment stage. The VB method is first incorporated into cubature particle filter (PF), of which the proposal distribution is set as cubature Kalman filter (CKF) to provide the accuracy and stable estimation for the application of navigation system. Then, the Kullback–Leibler distance (KLD) resampling method is merged into the VB-based cubature PF to supply sufficient particles that enhance the stability of the proposed filter. The numerical simulation demonstrates that the proposed filter does better at presenting accurate and stable estimation than the VB-based CKF, and the experiments verify the effectiveness of the proposed filter for the application of INS.