Towards Robust Bayesian Estimation for Linear Dynamical Systems

Xinpeng Liu; Xianqiang Yang

doi:10.1109/CFASTA57821.2023.10243200

Towards Robust Bayesian Estimation for Linear Dynamical Systems

Xinpeng Liu^*
, Xianqiang Yang

^*Corresponding author for this work

School of Astronautics

Dalian University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

This paper presents a robust Bayesian approach for identification of linear dynamical systems (LDS) with non-Gaussian observation noises. The Bayesian treatment of the identification problem is formulated by introducing suitable likelihood functions and prior information. The posterior distributions over unknown parameters and hidden states are jointly estimated under variational Bayesian (VB) framework, where the augmentation technique is adopted to make inference of the LDS with random parameters, and the parameter uncertainties can be also quantified by the variance statistics. Numerical studies are performed to confirm the effectiveness of the proposed method.

Original language	English
Title of host publication	Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	160-165
Number of pages	6
ISBN (Electronic)	9798350332162
DOIs	https://doi.org/10.1109/CFASTA57821.2023.10243200
State	Published - 2023
Event	2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023 - Qingdao, China Duration: 14 Jul 2023 → 16 Jul 2023

Publication series

Name	Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023

Conference

Conference	2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023
Country/Territory	China
City	Qingdao
Period	14/07/23 → 16/07/23

Keywords

System identification
linear dynamical systems (LDS)
robust estimation
variational Bayesian (VB)

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10.1109/CFASTA57821.2023.10243200

Cite this

Liu, X., & Yang, X. (2023). Towards Robust Bayesian Estimation for Linear Dynamical Systems. In Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023 (pp. 160-165). (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CFASTA57821.2023.10243200

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title = "Towards Robust Bayesian Estimation for Linear Dynamical Systems",

abstract = "This paper presents a robust Bayesian approach for identification of linear dynamical systems (LDS) with non-Gaussian observation noises. The Bayesian treatment of the identification problem is formulated by introducing suitable likelihood functions and prior information. The posterior distributions over unknown parameters and hidden states are jointly estimated under variational Bayesian (VB) framework, where the augmentation technique is adopted to make inference of the LDS with random parameters, and the parameter uncertainties can be also quantified by the variance statistics. Numerical studies are performed to confirm the effectiveness of the proposed method.",

keywords = "System identification, linear dynamical systems (LDS), robust estimation, variational Bayesian (VB)",

author = "Xinpeng Liu and Xianqiang Yang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023 ; Conference date: 14-07-2023 Through 16-07-2023",

year = "2023",

doi = "10.1109/CFASTA57821.2023.10243200",

language = "英语",

series = "Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "160--165",

booktitle = "Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023",

address = "美国",

}

Liu, X & Yang, X 2023, Towards Robust Bayesian Estimation for Linear Dynamical Systems. in Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 160-165, 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023, Qingdao, China, 14/07/23. https://doi.org/10.1109/CFASTA57821.2023.10243200

Towards Robust Bayesian Estimation for Linear Dynamical Systems. / Liu, Xinpeng; Yang, Xianqiang.
Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 160-165 (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Towards Robust Bayesian Estimation for Linear Dynamical Systems

AU - Liu, Xinpeng

AU - Yang, Xianqiang

PY - 2023

Y1 - 2023

N2 - This paper presents a robust Bayesian approach for identification of linear dynamical systems (LDS) with non-Gaussian observation noises. The Bayesian treatment of the identification problem is formulated by introducing suitable likelihood functions and prior information. The posterior distributions over unknown parameters and hidden states are jointly estimated under variational Bayesian (VB) framework, where the augmentation technique is adopted to make inference of the LDS with random parameters, and the parameter uncertainties can be also quantified by the variance statistics. Numerical studies are performed to confirm the effectiveness of the proposed method.

AB - This paper presents a robust Bayesian approach for identification of linear dynamical systems (LDS) with non-Gaussian observation noises. The Bayesian treatment of the identification problem is formulated by introducing suitable likelihood functions and prior information. The posterior distributions over unknown parameters and hidden states are jointly estimated under variational Bayesian (VB) framework, where the augmentation technique is adopted to make inference of the LDS with random parameters, and the parameter uncertainties can be also quantified by the variance statistics. Numerical studies are performed to confirm the effectiveness of the proposed method.

KW - System identification

KW - linear dynamical systems (LDS)

KW - robust estimation

KW - variational Bayesian (VB)

UR - https://www.scopus.com/pages/publications/85173626839

U2 - 10.1109/CFASTA57821.2023.10243200

DO - 10.1109/CFASTA57821.2023.10243200

M3 - 会议稿件

AN - SCOPUS:85173626839

T3 - Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023

SP - 160

EP - 165

BT - Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023

Y2 - 14 July 2023 through 16 July 2023

ER -

Liu X, Yang X. Towards Robust Bayesian Estimation for Linear Dynamical Systems. In Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 160-165. (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023). doi: 10.1109/CFASTA57821.2023.10243200

Towards Robust Bayesian Estimation for Linear Dynamical Systems

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