An integrated weights particle filter algorithm based on correlation particle estimation and sequential importance re-sampling

Author

Tao Zhang ; Daixi Shi ; Zhiyong Xie ; Jingyan Song

Author_Institution

Dept. of Autom., Tsinghua Univ., Beijing, China

Volume

03

fYear

2013

fDate

16-18 Dec. 2013

Firstpage

1189

Lastpage

1193

Abstract

In this paper, a new integrated weight particle filter (IWPF) algorithm is proposed based on the combination of correlation particle estimation (CPE) weight and sequential importance re-sampling (SIR) weight. This method can reduce degeneracy phenomenon and re-sampling times of traditional particle filter. By choosing the typical nonlinear system model, the simulation results show that IWPF performs better than CPE and SIR. In our simulation case, this method can provide a 15% increase of accuracy in state estimation and a 30% decrease of re-sampling times.

Keywords

correlation theory; importance sampling; particle filtering (numerical methods); state estimation; CPE; IWPF; SIR; correlation particle estimation weight; degeneracy phenomenon reduction; integrated weight particle filter algorithm; nonlinear system model; resampling time reduction; sequential importance resampling weight; state estimation; Equations; Estimation; Mathematical model; Monte Carlo methods; Particle filters; Signal processing algorithms; Simulation; correlation particle estimation; observation path similarity; particle filter; sequential importance re-sample; state estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing (CISP), 2013 6th International Congress on

Conference_Location

Hangzhou

Print_ISBN

978-1-4799-2763-0

Type

conf

DOI

10.1109/CISP.2013.6743852

Filename

6743852