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.

• Periodical: Applied Mechanics and Materials (Volumes 130-134)
• Pages: 3311-3315
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.130-134.3311
• Online since: October 2011
• Authors: Nai Gao Jin, Fei Mo Li, Zhao Xing Li
• Keywords: CUDA, Gaussian Particle Filter, Parallel Implementation, Qausi-Monte Carlo

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