Journal
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 63, Issue 2, Pages 594-601Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2017.2730480
Keywords
Adaptive filtering; inverse Wishart distribution; Kalman filtering; time-varying noise covariance matrices; variational Bayesian (VB)
Funding
- National Natural Science Foundation of China [61773133, 61633008]
- Natural Science Foundation of Heilongjiang Province [F2016008]
- Fundamental Research Founds for the Central University of Harbin Engineering University [HEUCFP201705, HEUCF041702]
- Ph.D. Student Research and Innovation Fund of the Fundamental Research Founds for the Central Universities [HEUGIP201706]
- China Scholarship Council Foundation
- Engineering and Physical Sciences Research Council of the U.K. [EP/K014307/1]
- Engineering and Physical Sciences Research Council [EP/K014307/2, EP/K014307/1] Funding Source: researchfish
- EPSRC [EP/K014307/1, EP/K014307/2] Funding Source: UKRI
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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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