Multiple Basic Proposal Distributions Model Based Sampling Particle Filter

Abstract

A hybrid sampling strategy is considered in multimode sampling based particle filter to alleviate the degeneracy as one of the most typical problems in the particle filter. However, to achieve high accuracy, expensive computation cost is inevitable when generating the hybrid distribution. To overcome this problem, a novel framework of particle filter is proposed in this paper with an improved hybrid sampling strategy. The main novelty is that this framework can simplify the generation of the hybrid distribution and makes the selection of particles more reasonable, in which the likelihood of particle is used to select the particles and determine the weights of multiple basic proposal distributions. Two simulation examples are implemented to test performances of the proposed filter algorithm. The obtained results show that the proposed framework has several superior performances in comparison with the standard particle filter, the unscented particle filter and the multimode sampling based particle filter.