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| Received:May 11, 2006Revised:March 16, 2007 |
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| Joint state and parameter estimation in particle filtering and stochastic optimization |
| Xiaojun YANG, Keyi XING, Kunlin SHI, Quan PAN |
| (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) |
| Abstract: |
| 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 Approximation (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 feasibility and efficiency of the proposed algorithm. |
| Key words: Parameter estimation Particle filtering Sequential Monte Carlo Simultaneous perturbation stochastic approximation Adaptive estimation |
