Hybrid Filter Based Expectation Maximization Algorithm for High-speed Train Modeling

被引：0
|
作者：
Wang C. [1 ]
Chen J. [2 ]
Xun J. [3 ]
Li K.-C. [4 ]
机构：
[1] School of Internet of Things, Jiangnan University, Wuxi
[2] School of Science, Jiangnan University, Wuxi
[3] State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing
[4] National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 12期
基金：
中国国家自然科学基金;
关键词：
EM algorithm; Hierarchical identification; Kalman filtering; Parameter estimation; Particle filtering;
D O I：
10.16383/j.aas.c190193
中图分类号：
学科分类号：
摘要：
For the special high-speed train model structure with hidden variables in the form of the single mass-point, a hybrid filter based expectation maximization (EM) algorithm is proposed. By employing the hierarchical identification theory, the high-speed train state-space model is decomposed into a linear subsystem and a nonlinear subsystem. Furthermore, the Kalman filter and the particle filter are provided to estimate the velocity and displacement, respectively. Finally, the parameters of subsystems are identified by using the EM algorithm. Compared to the classical methods, the proposed algorithm can produce high accuracy estimation with less computational effort. The simulation results verify the effectiveness of the algorithm. Copyright © 2019 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：2260 / 2267
页数：7
相关论文
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