NDLI: Robust filter design for asymmetric measurement noise using variational Bayesian inference

Content Provider	IET Digital Library
Author	Xu, Chen Zhao, Shunyi Ma, Yanjun Huang, Biao Liu, Fei
Abstract	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.
Starting Page	1656
Ending Page	1664
Page Count	9
ISSN	17518644
Volume Number	13
e-ISSN	17518652
Issue Number	Issue 11, Jul (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/13/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2018.6016
Journal	IET Control Theory & Applications
Publisher Date	2019-04-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Kalman Filter Asymmetric Measurement Noise Bayes Method Effective State Estimator Estimation Theory Filtering Method in Signal Processing Filtering Theory Industrial Processes Measurement Noise Noise Noise Statistics Probability Probable Outliers Robust Filter Design Signal Processing Theory State Estimation Statistics System State Time-varying Skew Variational Bayesian Inference Variational Bayesian Technique
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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