Combined Parameter and State Estimation in Particle Filtering

Author

Yang, Xiaojun ; Shi, Kunlin ; Huang, Tao ; Xing, Keyi

Author_Institution

Xi´´an Inst. of Electromech. Inf. Technol., Xi´´an

fYear

2007

fDate

May 30 2007-June 1 2007

Firstpage

1036

Lastpage

1039

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. 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.

Keywords

adaptive estimation; gradient methods; maximum likelihood estimation; nonlinear dynamical systems; particle filtering (numerical methods); state estimation; stochastic processes; adaptive estimation; cost function; gradient approximation; maximum-likelihood estimation; nonlinear dynamic system; parameter estimation; particle filtering; sequential Monte Carlo; state estimation; stochastic approximation; Adaptive estimation; Adaptive filters; Information filtering; Information filters; Maximum likelihood estimation; Nonlinear dynamical systems; Parameter estimation; Recursive estimation; State estimation; Yttrium; adaptive estimation; parameter estimation; particle filtering; sequential Monte Carlo;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation, 2007. ICCA 2007. IEEE International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4244-0818-4

Electronic_ISBN

978-1-4244-0818-4

Type

conf

DOI

10.1109/ICCA.2007.4376514

Filename

4376514