A new particle filter with GA-MCMC resampling

Author

Li, Cui-yun ; Ji, Hong-bing

Author_Institution

Xidian Univ., Xi´´an

Volume

1

fYear

2007

fDate

2-4 Nov. 2007

Firstpage

146

Lastpage

150

Abstract

Particle filtering shows great promise in addressing a wide variety of non-linear and/or non-Gaussian problem. A crucial issue in particle filtering is to remove degeneracy phenomenon and alleviate the sample impoverishment problem. In this paper, Variations, using techniques from the genetic algorithm with Markov chain Monte Carlo mutation, to standard PFprocedures are proposed to solve these problem simultaneously. The simulation results show that the new particle filter superiors to the standard particle filter and the other filters.

Keywords

Markov processes; Monte Carlo methods; genetic algorithms; particle filtering (numerical methods); GA-MCMC resampling; Markov Chain Monte Carlo; genetic algorithm; nonGaussian problem; nonlinear problem; particle filter; Filtering; Genetic algorithms; Genetic mutations; Monte Carlo methods; Notice of Violation; Particle filters; Pattern analysis; Pattern recognition; Sonar navigation; Wavelet analysis; Genetic algorithm; Markov Chain Monte Carlo; Particle filter; Resampling;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-1065-1

Electronic_ISBN

978-1-4244-1066-8

Type

conf

DOI

10.1109/ICWAPR.2007.4420653

Filename

4420653