快速Metropolis-Hastings变异的遗传重采样粒子滤波器

为了解决传统粒子滤波器粒子退化与贫乏问题,提出了快速变异的遗传重采样粒子滤波算法.该算法将快速Metropolis-Hastings(MH)移动作为遗传算法的变异算子,使得快速变异算子与传统交叉算子、传统选择算子组合为一种新的粒子重采样算法.快速MH变异能对粒子进行移动,使得粒子的稳定分布为目标的后验概率密度分布.快速变异能有效解决一般变异算法易发散的问题,可以更快地提取到反映目标概率特征的典型粒子.实验证明,基于快速MH变异的遗传重采样方法可以快速提高粒子的多样性,避免粒子退化,减小跟踪误差.

Genetic resampling particle filter based on fast Metropolis-Hastings mutation

A genetic resampling particle filter based on the fast mutation is proposed for removing the degeneracy phenomenon and alleviating the sample impoverishment problem in the particle filter.The algorithm chooses the fast Metropolis-Hastings(MH) moving as a mutation operator,and the traditional crossover and the traditional selection are combined with it to form a new resampling algorithm.The fast MH mutation moves the particles so that their stationary distribution is a target posterior density.The fast mutation can solve the divergence problem of the traditional mutation and produce the typical particle to reflect the features of a target density more quickly.The simulation results show that the genetic resampling based on the fast MH mutation can improve the variety of particles,remove the degeneracy phenomenon and reduce the tracking error.