Joint state and parameter estimation in particle filtering and stochastic optimization
Xiaojun YANG
Keyi XING
Kunlin SHI
Quan PAN
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 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 systerns.Simulation result demonstrates the feasibility and efficiency of the proposed algorithm.
机标关键词：
分类号：TP3(计算技术、计算机技术)
资助基金：国家自然科学基金(60404011)
论文发表日期：2008-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：6( 215-220 )
英文信息