摘要
针对粒子滤波的粒子退化现象及多样性损失问题,提出了一种新的基于优化的自适应遗传算法的粒子滤波算法。该算法首先依据每个采样时刻生成的粒子集合重要性权值作为适应度值,自适应的确定交叉、遗传的概率;然后对选出的粒子进行遗传操作,重新度量其粒子的权值并进行状态估计。该方法不仅保留了粒子的多样性,而且相对于普通的基于自适应遗传算法的粒子滤波算法,降低了高权值粒子交叉和变异的可能,使粒子的采样更接近于状态后验概率密度分布。实验结果表明,该算法有效提高了滤波精度。
A new particle filter algorithm based on the improved adaptive genetic algorithm was proposed for moving the degeneracy phenomenon and alleviating the sample impoverishment problem in the particle filter.At first,the algorithm used the importance weight of particles to weigh their fitness value and determined the probability of particles to experience genetic manipulation adaptively according to their fitness value.Then,it implemented the crossover and mutation operation to the samples selected,weighed the particles again and estimated the state.This method not only reserves diverse sex of particle,but also is more than the common particle filter algorithm based on the adaptive genetic algorithm to lowered high weighed particle＇s crossover and mutation possibility,make the sampling of particles distributed more close to the posterior density distribution of the state.The simulation results show that the proposed algorithm effectively improved the accuracy of filtering.
出处
《计算机安全》
2012年第3期13-16,共4页
Network & Computer Security
关键词
粒子滤波
自适应遗传算法
交叉概率
变异概率
particle filter
adaptive genetic algorithm
crossover possibility
mutation possibility
