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

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journal contribution
posted on 17.04.2019, 12:46 by Y Huang, Y Zhang, Z Wu, N Li, J Chambers
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.

History

Citation

IEEE Transactions on Automatic Control, 2018, 63 (2), pp. 594-601

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Automatic Control

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

0018-9286

Copyright date

2018

Available date

17/04/2019

Publisher version

https://ieeexplore.ieee.org/document/8025799/

Language

en