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access icon free Robust filter design for asymmetric measurement noise using variational Bayesian inference

  • Author(s): Chen Xu 1, 2 ; Shunyi Zhao 1, 2 ; Yanjun Ma 2 ; Biao Huang 1, 2 ; Fei Liu 1
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  • Source: Volume 13, Issue 11, 23 July 2019, p. 1656 – 1664
    DOI:  10.1049/iet-cta.2018.6016 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 04/09/2018, Accepted 12/04/2019, Revised 08/02/2019, Published 16/04/2019

To obtain an effective state estimator for industrial processes, estimator needs to be designed to match the characteristics of noise. In this study, a new filter is proposed focusing on asymmetric measurement noise with probable outliers. By learning a time-varying skew t distribution using the variational Bayesian technique, the authors' method can estimate the system state and update the noise statistics simultaneously. A numerical simulation, as well as an experiment on the hybrid tank system, is conducted to demonstrate the performance. It shows that the proposed filter is superior to the existing solutions, especially when the statistics of measurements noise are unknown.

Inspec keywords: Bayes methods; estimation theory; noise; probability; state estimation; adaptive Kalman filters; filtering theory

Other keywords: time-varying skew; system state; asymmetric measurement noise; robust filter design; variational Bayesian inference; probable outliers; measurements noise; industrial processes; effective state estimator; variational Bayesian technique; noise statistics

Subjects: Filtering methods in signal processing; Other topics in statistics; Other topics in statistics; Signal processing theory

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