Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/43820
Title: A Novel Adaptive Kalman Filter with Inaccurate Process and Measurement Noise Covariance Matrices
Authors: Huang, Y
Zhang, Y
Wu, Z
Li, N
Chambers, J
First Published: 1-Feb-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Transactions on Automatic Control, 2018, 63 (2), pp. 594-601
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.
DOI Link: 10.1109/TAC.2017.2730480
ISSN: 0018-9286
Links: https://ieeexplore.ieee.org/document/8025799/
http://hdl.handle.net/2381/43820
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2018, Institute of Electrical and Electronics Engineers (IEEE). Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)
Appears in Collections:Published Articles, Dept. of Engineering

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