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

Huang, Yulong; Zhang, Yonggang; Wu, Zhemin; Li, Ning; Chambers, Jonathon A.

doi:10.1109/tac.2017.2730480

articleIEEE Transactions on Automatic ControlSep 5, 2017GREEN OA

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

YHYulong Huang YZYonggang Zhang ZWZhemin Wu NLNing Li JAJonathon A. Chambers

Harbin Engineering University · Harbin Institute of Technology · +1 more institution

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Abstract

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.

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Topics & keywords

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Keywords

Covariance intersection
Covariance
Kalman filter
Ensemble Kalman filter
Estimation of covariance matrices
Covariance matrix
Algorithm
Invariant extended Kalman filter

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