Weight approaching particle filter and its application
加权逼近粒子滤波算法及其应用

To solve the problem of particle degeneracy, sample impoverishment and expensive computing cost in conventional particle filter, we propose the weight approaching particle filter(WAPF) to increase the particle diversity before resampling step. In the resampling step, particles are classified into two groups according to their particle-weights, and then the particles with the smaller weights are replaced by the mean of the two group particles, so that the particles can approach from the low likelihood region to the high likelihood region. The difference between the particle distribution produced by the behavior of mean approaching and the likelihood distribution is described by Kullback information. Kullback information decreases with increasing iteration degree, which proves that the new algorithm is rational. Similar to the carrier wave method, the chaotic perturbation resampling method adopts the chaotic variable with the property of global ergodicity to ameliorate the diversity of samples and reduce the computation load. Simulation results demonstrate the feasibility of the improved particle filter.