关键词: 粒子滤波,Metropolis-Hastings变异,粒子群优化,粒子重采样

Abstract: A particle swarm optimized resampling method for particle filter based on Metropolis-Hastings(MH) mutation was proposed for improving estimation performance and particle impoverishment problem in the particle filter．The new algorithm chooses the MH moving as a mutation operator of particle swarm optimized,combines the mutation operator with velocity-position searching progress,and generates the particles so that their stationary distribution is a target posterior density．The new algorithm solves the problem of particle divergence effectively,speeds up the convergence rate,and improves the estimation precision．The simulation results show that the PSO resampling based on MH mutation can remove the degeneracy phenomenon and improve the tracking estimating effects in non-line system.

Key words: Particle filter,Metropolis-hastings mutation,Particle swarm optimized,Particle resampling