A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices

In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for linear Gaussian state-space models with inaccurate process and measurement noise covariance matrices is proposed. By choosing inverse Wishart priors, the state together with the predicted error and measurement...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 63; no. 2; pp. 594 - 601
Main Authors Huang, Yulong, Zhang, Yonggang, Wu, Zhemin, Li, Ning, Chambers, Jonathon
Format Journal Article
LanguageEnglish
Published IEEE 01.02.2018
Subjects
Adaptive filtering
Bayes methods
Covariance matrices
Estimation error
inverse Wishart distribution
Kalman filtering
Kalman filters
Noise measurement
State-space methods
Target tracking
time-varying noise covariance matrices
variational Bayesian (VB)
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Summary:In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for linear Gaussian state-space models with inaccurate process and measurement noise covariance matrices is proposed. By choosing inverse Wishart priors, the state together with the predicted error and measurement noise covariance matrices are inferred based on the VB approach. Simulation results for a target tracking example illustrate that the proposed VBAKF has better robustness to resist the uncertainties of process and measurement noise covariance matrices than existing state-of-the-art filters.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2017.2730480