Self-Tuning Unbiased Finite Impulse Response Filtering Algorithm for Processes With Unknown Measurement Noise Covariance
- Authors
- Zhao, Shunyi; Shmaliy, Yuriy S.; Ahn, Choon Ki; Liu, Fei
- Issue Date
- 5월-2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Averaging horizon; Kalman filter (KF); state estimation; unbiased finite impulse response (UFIR) filter; variational Bayesian (VB) approach
- Citation
- IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, v.29, no.3, pp.1372 - 1379
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
- Volume
- 29
- Number
- 3
- Start Page
- 1372
- End Page
- 1379
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/128193
- DOI
- 10.1109/TCST.2020.2991609
- ISSN
- 1063-6536
- Abstract
- An unbiased finite impulse response (UFIR) filtering algorithm is designed in the discrete-time state-space for industrial processes with unknown measurement data covariance. By assuming an inverse-Wishart distribution, the data noise covariance is recursively estimated using the variational Bayesian (VB) approach. The optimal averaging horizon length N-opt is estimated in real time by incorporating the estimated data noise covariance into the full-horizon UFIR filter and specifying N-opt at a point, where the estimation error covariance reaches a minimum. The proposed VB-UFIR algorithm is applied to a quadrupled water tank system and moving target tracking. It is demonstrated that the VB-UFIR filter self-estimates N-opt more accurately than known solutions. Furthermore, the VB-UFIR filter is not prone to divergence and produces more stable and more reliable estimates than the VB-Kalman filter.
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