Title :
A new particle filter with GA-MCMC resampling
Author :
Li, Cui-yun ; Ji, Hong-bing
Author_Institution :
Xidian Univ., Xi´´an
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;
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
DOI :
10.1109/ICWAPR.2007.4420653