Print Email Facebook Twitter Adapting Particle Filter Algorithms to the GPU Architecture Title Adapting Particle Filter Algorithms to the GPU Architecture Author Chitchian, M.M. Contributor Van Amesfoort, A. (mentor) Simonetto, A. (mentor) Kevizcky, T. (mentor) Sips, H.J. (mentor) Faculty Electrical Engineering, Mathematics and Computer Science Department Software Technology Programme Parallel and Distributed Systems Date 2011-05-20 Abstract The particle filter is a Bayesian estimation technique based on Monte Carlo simulations. The non-parametric nature of particle filters makes them ideal for non-linear non-Gaussian systems. This greater filtering accuracy, however, comes at the price of increased computational complexity which limits their practical use for real-time applications. This thesis presents an attempt to enable real-time particle filtering for complex estimation problems using modern GPU hardware. We propose a GPU-based generic particle filtering framework which can be applied to various estimation problems. We implement a real-time estimation application using this particle filtering framework and measure the estimation error with different filter parameters. Furthermore, we present an in-depth performance analysis of our GPU implementation followed by a number of optimisations in order to increase implementation efficiency. Subject particle filterbayesian estimationCUDAGPGPUGPUbayes filternon-linear estimationdistributed particle filterreal-time estimationparallel particle filter To reference this document use: http://resolver.tudelft.nl/uuid:9b11258e-cd9c-494d-bbb7-3b85c700b30f Part of collection Student theses Document type master thesis Rights (c) 2011 Chitchian, M.M. Files PDF MScThesisMehdiChitchian.pdf 4.06 MB Close viewer /islandora/object/uuid:9b11258e-cd9c-494d-bbb7-3b85c700b30f/datastream/OBJ/view