Paper Title:
Quasi-Monte Carlo Gaussian Particle Filtering Acceleration Using CUDA
  Abstract

A CUDA accelerated Quasi-Monte Carlo Gaussian particle filter (QMC-GPF) is proposed to deal with real-time non-linear non-Gaussian problems. GPF is especially suitable for parallel implementation as a result of the elimination of resampling step. QMC-GPF is an efficient counterpart of GPF using QMC sampling method instead of MC. Since particles generated by QMC method provides the best-possible distribution in the sampling space, QMC-GPF can make more accurate estimation with the same number of particles compared with traditional particle filter. Experimental results show that our GPU implementation of QMC-GPF can achieve the maximum speedup ratio of 95 on NVIDIA GeForce GTX 460.

  Info
Periodical
Edited by
Han Zhao
Pages
3311-3315
DOI
10.4028/www.scientific.net/AMM.130-134.3311
Citation
N. G. Jin, F. M. Li, Z. X. Li, "Quasi-Monte Carlo Gaussian Particle Filtering Acceleration Using CUDA", Applied Mechanics and Materials, Vols. 130-134, pp. 3311-3315, 2012
Online since
October 2011
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