Particle filter based on iterated importance density function and parallel resampling
来源期刊：中南大学学报(英文版)2015年第9期
论文作者：WU Yong WANG Jun CAO Yun-he
文章页码：3427 - 3439
Key words：particle filter; iterated importance density function; least squares estimate; parallel resampling; graphics processing unit
Abstract: The design, analysis and parallel implementation of particle filter (PF) were investigated. Firstly, to tackle the particle degeneracy problem in the PF, an iterated importance density function (IIDF) was proposed, where a new term associating with the current measurement information (CMI) was introduced into the expression of the sampled particles. Through the repeated use of the least squares estimate, the CMI can be integrated into the sampling stage in an iterative manner, conducing to the greatly improved sampling quality. By running the IIDF, an iterated PF (IPF) can be obtained. Subsequently, a parallel resampling (PR) was proposed for the purpose of parallel implementation of IPF, whose main idea was the same as systematic resampling (SR) but performed differently. The PR directly used the integral part of the product of the particle weight and particle number as the number of times that a particle was replicated, and it simultaneously eliminated the particles with the smallest weights, which are the two key differences from the SR. The detailed implementation procedures on the graphics processing unit of IPF based on the PR were presented at last. The performance of the IPF, PR and their parallel implementations are illustrated via one-dimensional numerical simulation and practical application of passive radar target tracking.
WU Yong(武勇), WANG Jun(王俊), CAO Yun-he(曹运合)
(National Laboratory of Radar Signal Processing (Xidian University), Xi’an 710071, China)
Abstract:The design, analysis and parallel implementation of particle filter (PF) were investigated. Firstly, to tackle the particle degeneracy problem in the PF, an iterated importance density function (IIDF) was proposed, where a new term associating with the current measurement information (CMI) was introduced into the expression of the sampled particles. Through the repeated use of the least squares estimate, the CMI can be integrated into the sampling stage in an iterative manner, conducing to the greatly improved sampling quality. By running the IIDF, an iterated PF (IPF) can be obtained. Subsequently, a parallel resampling (PR) was proposed for the purpose of parallel implementation of IPF, whose main idea was the same as systematic resampling (SR) but performed differently. The PR directly used the integral part of the product of the particle weight and particle number as the number of times that a particle was replicated, and it simultaneously eliminated the particles with the smallest weights, which are the two key differences from the SR. The detailed implementation procedures on the graphics processing unit of IPF based on the PR were presented at last. The performance of the IPF, PR and their parallel implementations are illustrated via one-dimensional numerical simulation and practical application of passive radar target tracking.
Key words:particle filter; iterated importance density function; least squares estimate; parallel resampling; graphics processing unit
