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Joint state and parameter estimation in particle filtering and stochastic optimization 被引量：2

Joint state and parameter estimation in particle filtering and stochastic optimization
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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 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
作者 Xiaojun YANG Keyi XING Kunlin SHI Quan PAN
机构地区 The State Key Laboratory for Manufacturing System Engineering Xi＇an Institute of Electromechanical Information Technology School of Automation
出处 《控制理论与应用（英文版）》 EI 2008年第2期215-220,共6页
基金 the National Natural Science Foundation of China (No. 60404011)
关键词 Parameter estimation Particle filtering Sequential Monte Carlo Simultaneous perturbation stochastic approximation Adaptive estimation Parameter estimation Particle filtering Sequential Monte Carlo Simultaneous perturbation stochastic approximation Adaptive estimation
分类号 TP13 [自动化与计算机技术—控制理论与控制工程]
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参考文献11

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二级参考文献70

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控制理论与应用（英文版）
2008年 第2期

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