A Gaussian Sum Smoothing algorithm for Hammerstein-Wiener State-Space Systems

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

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Resumen

In this paper, we develop a novel Bayesian smoothing method for obtaining the smoothed probability density functions of Hammerstein-Wiener state-space systems and the corresponding state estimation. The proposed smoother is designed using the two-filter approach, based on the Gaussian sum filtering algorithm and a backward filtering method. In this work, this backward filter is obtained using an approximation of the probability function of the non-linear output conditioned to the system state. Both the forward filter and the backward filter are used to obtain the Gaussian sum smoothing algorithm, which also includes the computation of the joint probability density function of the system state in two consecutive time instants. Numerical examples are presented to illustrate the benefits of our proposal.

Idioma originalInglés
Título de la publicación alojada2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control
Subtítulo de la publicación alojadaFor the Development of Sustainable Agricultural Systems, ICA-ACCA 2022
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665494083
DOI
EstadoPublicada - 2022
Evento2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2022 - Virtual, Online, Chile
Duración: 24 oct. 202228 oct. 2022

Serie de la publicación

Nombre2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control: For the Development of Sustainable Agricultural Systems, ICA-ACCA 2022

Conferencia

Conferencia2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2022
País/TerritorioChile
CiudadVirtual, Online
Período24/10/2228/10/22

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