Particle filter improved by genetic algorithm and particle swarm optimization algorithm

doi:10.4304/jsw.8.3.666-672

IR > 计算机与通信学院

	Particle filter improved by genetic algorithm and particle swarm optimization algorithm
	Li, Ming; Pang, Bo; He, Yongfeng; Nian, Fuzhong
	2013
发表期刊	Journal of Software
ISSN	1796217X
卷号	8 期号:3 页码:666-672
摘要	Particle filter algorithm is a filtering method which uses Monte Carlo idea within the framework of Bayesian estimation theory. It approximates the probability distribution by using particles and discrete random measure which is consisted of their weights, it updates new discrete random measure recursively according to the algorithm. When the sample is large enough, the discrete random measure approximates the true posteriori probability density function of the state variable. The particle filter algorithm is applicable to any non-linear non-Gaussian system. But the standard particle filter does not consider the current measured value, which will lead to particles with non-zero weights become less after some iterations, this results in particle degradation; re-sampling technique was used to inhibit degradation, but this will reduce the particle diversity, and results in particle impoverishment. To overcome the problems, this paper proposed a new particle filter which introduced genetic algorithm and particle swarm optimization algorithm. The new algorithm is called intelligent particle filter (IPF). Driving particles move to the optimal position by using particle swarm optimization algorithm, thus the numbers of effective particles was increased, the particle diversity was improved, and the particle degradation was inhibited. Replace the re-sampling method in traditional particle filter by using the choice, crossover and mutation operation of the genetic algorithm, avoiding the phenomenon of impoverishment. Simulation results show that the new algorithm improved the estimation accuracy significantly compare with the standard particle filter. © 2013 ACADEMY PUBLISHER.
关键词	Bayesian networks Genetic algorithms Particle swarm optimization (PSO) Probability density function Probability distributions Bayesian estimation theory Crossover and mutation Genetic algorithm and particle swarm optimizations Non-linear non-Gaussian Particle degeneracy Particle filter Particle filter algorithms Particle swarm optimization algorithm
DOI	10.4304/jsw.8.3.666-672
收录类别	EI
语种	英语
出版者	Academy Publisher
EI入藏号	20131216135122
EI主题词	Monte Carlo methods
EI分类号	723 Computer Software, Data Handling and Applications - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 922.1 Probability Theory - 922.2 Mathematical Statistics
来源库	Compendex
分类代码	723 Computer Software, Data Handling and Applications - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 922.1 Probability Theory - 922.2 Mathematical Statistics
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/113162
专题	计算机与通信学院
作者单位	School of Computer and Communication, LanZhou University of Technology, LanZhou, China
第一作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Li, Ming,Pang, Bo,He, Yongfeng,et al. Particle filter improved by genetic algorithm and particle swarm optimization algorithm[J]. Journal of Software,2013,8(3):666-672.
APA	Li, Ming,Pang, Bo,He, Yongfeng,&Nian, Fuzhong.(2013).Particle filter improved by genetic algorithm and particle swarm optimization algorithm.Journal of Software,8(3),666-672.
MLA	Li, Ming,et al."Particle filter improved by genetic algorithm and particle swarm optimization algorithm".Journal of Software 8.3(2013):666-672.