A Novel Filtering Method for Hammerstein-Wiener State-Space Systems

Angel L. Cedeno, Rodrigo Carvajal, Juan C. Aguero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we develop a novel filtering algorithm for Hammerstein-Wiener State-Space Systems. The likelihood function of the noisy nonlinear output signal given the system state is approximated by a Gaussian-Legendre quadrature rule. This approximation produces an explicit model of this likelihood function with a Gaussian Sum structure. Based on the general Bayesian filtering framework, we develop a Gaussian Sum Filter algorithm to obtain the a posteriori probability density function of the state given the current and past nonlinear output. With the characterization of the a posteriori probability function, we can obtain the associated statistics of the state. Finally, we present numerical examples to illustrate the benefits of our proposal.

Original languageEnglish
Title of host publication2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665408738
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021 - Virtual, Online, Chile
Duration: 6 Dec 20219 Dec 2021

Publication series

Name2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021

Conference

Conference2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
Country/TerritoryChile
CityVirtual, Online
Period6/12/219/12/21

Keywords

  • Gaussian quadrature
  • Gaussian sum filter
  • Hammerstein-Wiener systems
  • State estimation

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