A Novel Adaptive Kalman Filter with Inaccurate Process and Measurement Noise Covariance Matrices
journal contributionposted on 2019-04-17, 12:46 authored 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.
CitationIEEE Transactions on Automatic Control, 2018, 63 (2), pp. 594-601
Author affiliation/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering
- AM (Accepted Manuscript)