A Novel Progressive Gaussian Approximate Filter with Variable Step Size Based on a Variational Bayesian Approach
Mingming Bai, Yulong Huang, Yonggang Zhang, Lyudmila Mihaylova, Jonathon A. Chambers
Abstract
The selection of step sizes in the progressive Gaussian approximate filter (PGAF) is important, and it is difficult to select optimal values in practical applications. Furthermore, in the PGAF, significant integral approximation errors are generated by the repeated approximate calculations of the Gaussian weighted integrals, which results in an inaccurate measurement noise covariance matrix (MNCM). To solve these problems, in this paper, the step sizes and the MNCM are jointly estimated based on the variational Bayesian (VB) approach. By incorporating the adaptive estimates of step sizes and the MNCM into the PGAF framework, a novel PGAF with variable step size is proposed. Simulation results illustrate that the proposed filter has higher estimation accuracy than existing state-of-the-art nonlinear Gaussian approximate filters.
BibTeX
@inproceedings{icassp2019_anovelprogressiv,
title = {A Novel Progressive Gaussian Approximate Filter with Variable Step Size Based on a Variational Bayesian Approach},
author = {Mingming Bai and Yulong Huang and Yonggang Zhang and Lyudmila Mihaylova and Jonathon A. Chambers},
booktitle = {ICASSP 2019},
year = {2019}
}