Particle Filtering with Multi Proposal Distributions

Fasheng WANG, Qingjie ZHAO, Yingbo ZHANG, Liqun ZHANG

Beijing Key Lab of Intelligent Information.
Neusoft Institute of Information, Northeastern University, P.R.China.

DOI: 10.4236/ijcns.2008.11004                

Abstract

Particle filtering algorithm has been applied to various fields due to its capacity to handle nonlinear/non-Gaussian dynamic problems. One crucial issue in particle filtering is the selection of the proposal distribution that generates the particles. In this paper, we give a novel strategy for selecting proposal distribution. Firstly, divide-conquer strategy is used, in which the particles used are divided into several parts. Afterward, different parts of particles are drawn from different proposal distributions. People can flexibly adjust how many of the particles drawn from specific proposal distributions according to their idiographic requirements. We provide simulation results that show its efficiency and performance.

Keywords

Sequential Monte Carlo, Proposal Distribution, Multiple Proposal Distributions

Conflicts of Interest

The authors declare no conflicts of interest.

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