Identification of Nonlinear State-Space Systems With Skewed Measurement Noises

作     者：Liu, Xinpeng Yang, Xianqiang 

作者机构：Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Minist Educ Dalian 116024 Peoples R China Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150001 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS》 (IEEE Trans. Circuits Syst. Regul. Pap.)

年 卷 期：2022年第69卷第11期

页      面：4654-4662页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Natural Science Foundation of China Excellent Youth Project of Natural Science Foundation of Heilongjiang Province [YQ2021F009] 

主　　题：Noise measurement State-space methods State estimation Gaussian distribution Smoothing methods Robustness Monte Carlo methods Nonlinear system identification skewed noise generalized hyperbolic skew Student's t-distribution expectation-maximization algorithm 

摘      要：In this paper, we consider the identification problem for nonlinear state-space models with skewed measurement noises. The generalized hyperbolic skew Student s t (GHSkewt) distribution is employed to describe the skewed noises and formulate the hierarchical model of the considered system. A unified framework for estimating unknown states and model parameters is presented based on expectation-maximization (EM) algorithm, in which the forward filtering backward simulation with rejection sampling (RS-FFBSi) is employed to efficiently estimate the smoothing densities of the hidden states, and optimization method is adopted to update model parameters. One numerical study and the electro-mechanical positioning system (EMPS) are employed to verify the effectiveness of the developed approach.