DocumentCode :
2594968
Title :
Evolutionary particle filter: re-sampling from the genetic algorithm perspective
Author :
Kwok, N.M. ; Fang, Gu ; Zhou, Weizhen
Author_Institution :
ARC Centre of Excellence for Autonomous Syst., Univ. of Technol., Sydney, NSW, Australia
fYear :
2005
fDate :
2-6 Aug. 2005
Firstpage :
2935
Lastpage :
2940
Abstract :
The sample impoverishment problem in particle filters is investigated from the perspective of genetic algorithms. The contribution of this paper is in the proposal of a hybrid technique to mitigate sample impoverishment such that the number of particles required and hence the computation complexities are reduced. Studies are conducted through the use of Chebyshev inequality for the number of particles required. The relationship between the number of particles and the time for impoverishment is examined by considering the takeover phenomena as found in genetic algorithms. It is revealed that the sample impoverishment problem is caused by the resampling scheme in implementing the particle filter with a finite number of particles. The use of uniform or roulette-wheel sampling also contributes to the problem. Crossover operators from genetic algorithms are adopted to tackle the finite particle problem by re-defining or re-supplying impoverished particles during filter iterations. Effectiveness of the proposed approach is demonstrated by simulations for a monobot simultaneous localization and mapping application.
Keywords :
Chebyshev approximation; computational complexity; genetic algorithms; particle filtering (numerical methods); sampling methods; Chebyshev inequality; computation complexity; crossover operators; evolutionary particle filter; finite particle problem; genetic algorithm; resampling scheme; roulette-wheel sampling; sample impoverishment problem; Australia; Bayesian methods; Boosting; Computational complexity; Design engineering; Genetic algorithms; Genetic engineering; Particle filters; Sampling methods; Smoothing methods; genetic algorithms; particle filter; re-sampling; selection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on
Print_ISBN :
0-7803-8912-3
Type :
conf
DOI :
10.1109/IROS.2005.1545119
Filename :
1545119
Link To Document :
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