Study of State Estimation with Super Particle Filter

Author

Liu, Tianjian ; Zhang, Xuping ; Zhu, Shanan

Author_Institution

Coll. of Inf., Zhejiang Univ., Hangzhou

Volume

1

fYear

0

fDate

0-0 0

Firstpage

1434

Lastpage

1437

Abstract

Model with nonlinear and time variance is often met in the question of time sequence state estimation. It is not very good to solve it by EKF algorithm. We propose a new algorithm, super particle filter (SPF), which adds hyper parameters in state vector and estimates state and parameters simultaneously online. By the method, hype parameters can be adjusted to change with model automatically. The introduction of hyper parameters to state vector makes state space model nonlinear. For the reason, particle filter is applied to solve the nonlinear and non-Gaussian state space models. We compared this algorithm to the EKF algorithm. Experimental results show SPF algorithm increase 60% in accurate and 70% in time expenditure

Keywords

Kalman filters; parameter estimation; particle filtering (numerical methods); state estimation; extended Kalman filtering; parameter estimation; state vector; super particle filter; time sequence state estimation; time variance; Automation; Educational institutions; Gold; Intelligent control; Parameter estimation; Particle filters; Reactive power; State estimation; State-space methods; Parameter estimation; State etimation; Super Particle Filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712585

Filename

1712585