Joint state and parameter estimation in particle filtering and stochastic optimization

作     者：Xiaojun YANG Keyi XING Kunlin SHI Quan PAN 

作者机构：The State Key Laboratory for Manufacturing System Engineering System Engineering Institute Xi＇an Jiaotong University Xi＇an Shaanxi 710049 China Xi＇an Institute of Electromechanical Information Technology Xi＇an Shaanxi 710065 China School of Automation Northwestern polytechnical University Xi＇an Shaanxi 710072 China 

出 版 物：《控制理论与应用（英文版）》 (J. Control Theory Appl.)

年 卷 期：2008年第6卷第2期

页      面：215-220页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：the National Natural Science Foundation of China (No. 60404011) 

主　　题：Parameter estimation Particle filtering Sequential Monte Carlo Simultaneous perturbation stochastic approximation Adaptive estimation 

摘      要：In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering and Simultaneous Perturbation Stochastic Approxi- mation （SPSA） technique. The estimations of parameters are obtained by maximum-likelihood estimation and sampling within particle filtering framework, and the SPSA is used for stochastic optimization and to approximate the gradient of the cost function. The proposed algorithm achieves combined estimation of dynamic state and static parameters of nonlinear systems. Simulation result demonstrates the feasibilitv and efficiency of the proposed algorithm