An Improved Variational Bayesian Adaptive Robust Kalman Filter

Ding Li; Xudong Yu; Pengfei Jiang; Wanli Jian

doi:10.1109/MLCCIM55934.2022.00023

2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM)

An Improved Variational Bayesian Adaptive Robust Kalman Filter

Year: 2022, Pages: 93-98

DOI Bookmark: 10.1109/MLCCIM55934.2022.00023

Authors

Ding Li, College of Advanced Interdisciplinary Studies, National University of Defense Technology,Changsha,China
Xudong Yu, College of Advanced Interdisciplinary Studies, National University of Defense Technology,Changsha,China
Pengfei Jiang, College of Intelligence Sciences and Technology, National University of Defense Technology,Changsha,China
Wanli Jian, College of Intelligence Sciences and Technology, National University of Defense Technology,Changsha,China

Abstract

Inaccurate or vague system noise model (Q and R) of the Kalman filter (KF) will seriously affect the accuracy of filter estimation. In this paper, the noise model is adapted by the variational Bayesian method, and the outliers in the measurement are eliminated by the Mahalanobis distance criterion method. The proposed IVBARKF inherits the excellent adaptability of the variational Bayesian method and the strong robustness of the Mahalanobis distance criterion. The effectiveness is verified by the target tracking experiment. The proposed method has better performance than conventional method when the noise model is inaccurate and contaminated by outliers.

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An Improved Variational Bayesian Adaptive Robust Kalman Filter

Authors

Abstract

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