School of Computer and Communication, LanZhou University of Technology, LanZhou , China

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Abstract—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.

Index Terms—Particle Filter; Particle Swarm Optimization; Genetic algorithm; Particle Degeneracy; Particle Impoverishment.

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Cite: Ming Li, Bo Pang, Yongfeng He, Fuzhong Nian, "Particle Filter Improved by Genetic Algorithm and Particle Swarm Optimization Algorithm," Journal of Software vol. 8, no. 3, pp. 666-672, 2013.