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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Mechanical systems and signal processing Ročník 236; s. 112912
Hlavní autoři: Wang, Wenjie, Liu, Siyu, Jiang, Yonghua, Sun, Jianfeng, Xu, Wanxiu, Chen, Xiaohao, Dong, Zhilin, Jiao, Weidong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.08.2025
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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