Robust Identification of Black-box Nonlinear SSM Using Expectation-maximization

Authors

• Xiaonan Li, Control and Simulation Center, Harbin Institute of Technology, Harbin 150001, China; National Key Laboratory of Air-based Information Perception and Fusion, Luoyang 471009, China
• Tao Chao, Control and Simulation Center, Harbin Institute of Technology, Harbin 150001, China; National Key Laboratory of Modeling and Simulation for Complex Systems, Harbin 150001, China
• Ping Ma, Control and Simulation Center, Harbin Institute of Technology, Harbin 150001, China; National Key Laboratory of Modeling and Simulation for Complex Systems, Harbin 150001, China
• Ming Yang, Control and Simulation Center, Harbin Institute of Technology, Harbin 150001, China; National Key Laboratory of Modeling and Simulation for Complex Systems, Harbin 150001, China
• Yuxuan Wang, National Key Laboratory of Air-based Information Perception and Fusion, Luoyang 471009, China; China Airborne Missile Academy, Luoyang 471009, China

Abstract

To address the robust identification problem of nonlinear state space models (SSM) with outliers, missing observations, and unknown state equations, this paper proposes a modeling method based on eigenfunction expansion, Gaussian-process state-space models (GP-SSM), and Student-t distribution. The proposed approach consists of: modeling the state transition function using eigenfunctions and pre-encoding the priors of basis function coefficients via GP-SSM to enhance flexibility; modeling observations as a Student-t distribution with unknown parameters to enhance robustness against outliers; proposing the enhanced particle Gibbs with ancestor sampling (EPGAS) algorithm to adapt to state estimation in scenarios with missing observations; and deriving unknown model parameters based on the expectation maximization (EM) method.The simulation examples and benchmark model test results show that the proposed method has better performance compared to existing literature methods, and can significantly improve the model identification accuracy when there are outliers and missing observations.

Recommended Citation

Li, Xiaonan; Chao, Tao; Ma, Ping; Yang, Ming; and Wang, Yuxuan (2026) "Robust Identification of Black-box Nonlinear SSM Using Expectation-maximization," Journal of System Simulation: Vol. 38: Iss. 5, Article 2.
DOI: 10.16182/j.issn1004731x.joss.25-0736
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss5/2

First Page

1146

Last Page

1158

CLC

TP391.9

Recommended Citation

Li Xiaonan, Chao Tao, Ma Ping, et al. Robust Identification of Black-box Nonlinear SSM Using Expectation-maximization[J]. Journal of System Simulation, 2026, 38(5): 1146-1158.

Corresponding Author

Chao Tao

DOI

10.16182/j.issn1004731x.joss.25-0736