An improved particle filter based on genetic resampling

Abstract

The resampling of particle filter algorithm causes sample impoverishment and results in the loss of diversity. An improved particle filter algorithm is proposed by combining genetic algorithm and PF. First of all, particles are selected according to the probability with the principle of selection in genetic algorithm which make particles with larger weights to be selected more likely , and the proposed algorithm can guide the entire process to the direction of evolution. Crossover and mutation are adopted to replace the strategy of resampling which simply copy particles with high weight, delete particles with low weight and implement the particle update and optimization. Finally, the simulation experiments show that the proposed algorithm can more effectively improve the diversity of particles.