Title :
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
fDate :
May 30 2007-June 1 2007
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;
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
DOI :
10.1109/ICCA.2007.4376514
