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Robust filter design for asymmetric measurement noise using variational Bayesian inference

Robust filter design for asymmetric measurement noise using variational Bayesian inference

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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.

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