Self-Tuning Unbiased Finite Impulse Response Filtering Algorithm for Processes With Unknown Measurement Noise Covariance

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 Bayesia...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2021-05, Vol.29 (3), p.1372-1379
Main Authors: Zhao, Shunyi, Shmaliy, Yuriy S., Ahn, Choon Ki, Liu, Fei
Format: Article
Language:English
Subjects:
Algorithms
Averaging horizon
Bayes methods
Covariance
Estimation
Horizon
Impulse response
Kalman filter (KF)
Kalman filters
Moving targets
Noise
Noise measurement
Optimized production technology
Process control
Real-time systems
Self tuning
state estimation
Tracking
unbiased finite impulse response (UFIR) filter
variational Bayesian (VB) approach
Water tanks
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Summary: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_{\mathrm {opt}} is estimated in real time by incorporating the estimated data noise covariance into the full-horizon UFIR filter and specifying N_{\mathrm {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_{\mathrm {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.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2020.2991609