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A Novel Adaptive Kalman Filter with Inaccurate Process and Measurement Noise Covariance Matrices
journal contribution
posted on 2019-04-17, 12:46 authored by Y Huang, Y Zhang, Z Wu, N Li, J ChambersIn 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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Citation
IEEE Transactions on Automatic Control, 2018, 63 (2), pp. 594-601Author affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of EngineeringVersion
- AM (Accepted Manuscript)
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IEEE Transactions on Automatic ControlPublisher
Institute of Electrical and Electronics Engineers (IEEE)issn
0018-9286Copyright date
2018Available date
2019-04-17Publisher DOI
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https://ieeexplore.ieee.org/document/8025799/Language
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