Combined parameter and state estimation in particle filtering

被引：0
|
作者：
Yang, Xiaojun [1 ,2 ]
Shi, Kunlin [1 ]
Huang, Tao [1 ]
Xing, Keyi [2 ]
机构：
[1] Xian Inst Electromech Informat Technol, Xian, Peoples R China
[2] Xi An Jiao Tong Univ, Syst Engn Inst, Xian, Peoples R China
来源：
2007 IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1-7 | 2007年
关键词：
parameter estimation; particle filtering; adaptive estimation; sequential Monte Carlo;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
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. The estimates of static parameters are obtained by state samples and maximum-likelihood estimation in particle filtering, and the stochastic approximation is used to approximate the gradient of cost function. The proposed algorithm achieves combined state and parameters estimation. Simulation result demonstrates the efficiency of the algorithm.
引用
收藏
页码：1614 / +
页数：2
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