DocumentCode :
2630514
Title :
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
Link To Document :
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