A robust filter and smoother-based expectation–maximization algorithm for bilinear systems with heavy-tailed noise

This paper focuses on a specific type of nonlinear systems—bilinear systems and introduces a robust filter and smoother-based expectation–maximization (RFS-EM) algorithm that enables joint estimation of states and parameters in the presence of heavy-tailed noise. Specifically, to mitigate the impact...

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Bibliographic Details
Published in:Mechanical systems and signal processing Vol. 236; p. 112912
Main Authors: Wang, Wenjie, Liu, Siyu, Jiang, Yonghua, Sun, Jianfeng, Xu, Wanxiu, Chen, Xiaohao, Dong, Zhilin, Jiao, Weidong
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.08.2025
Subjects:
Bilinear system
Heavy-tailed noise
Parameter estimation
Robust filter
State estimation
Student’s t distribution
Bilinear system
Parameter estimation
Robust filter
State estimation
Student’s t distribution
Heavy-tailed noise
ISSN:0888-3270
Online Access:Get full text
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Summary:This paper focuses on a specific type of nonlinear systems—bilinear systems and introduces a robust filter and smoother-based expectation–maximization (RFS-EM) algorithm that enables joint estimation of states and parameters in the presence of heavy-tailed noise. Specifically, to mitigate the impact of heavy-tailed noise, this study explores a combination method of robust filter and smoother based on Student’s t distribution, integrating it into an expectation–maximization framework. In the expectation step, forward and backward predictions of system states are performed using the robust filter and smoother. Following this, in the maximization step, system parameters are estimated through numerical optimization. The proposed RFS-EM achieves joint estimation of the states and parameters for bilinear systems. Finally, a numerical simulation and a DC motor simulation validate the effectiveness of the proposed algorithm.
ISSN:0888-3270
DOI:10.1016/j.ymssp.2025.112912