TAILIEUCHUNG - Báo cáo sinh học: " Research Article Particle Filtering: The Need for Speed"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Particle Filtering: The Need for Speed | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 181403 9 pages doi 2010 181403 Research Article Particle Filtering The Need for Speed Gustaf Hendeby 1 Rickard Karlsson 2 and Fredrik Gustafsson EURASIP Member 3 1 Department of Augmented Vision German Research Center for Artificial Intelligence 67663 Kaiserslatern Germany 2NIRA Dynamics AB Teknikringen 6 58330 Linkoping Sweden 3 Department of Electrical Engineering Linkoping University 58183 Linkoping Sweden Correspondence should be addressed to Rickard Karlsson rickard@ Received 22 February 2010 Accepted 26 May 2010 Academic Editor Abdelak Zoubir Copyright 2010 Gustaf Hendeby et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. The particle filter PF has during the last decade been proposed for a wide range of localization and tracking applications. There is a general need in such embedded system to have a platform for efficient and scalable implementation of the PF. One such platform is the graphics processing unit GPU originally aimed to be used for fast rendering of graphics. To achieve this GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU GPGPU as a complement to the central processing unit CPU . In this paper GPGPU techniques are used to make a parallel recursive Bayesian estimation implementation using particle filters. The modifications made to obtain a parallel particle filter especially for the resampling step are discussed and the performance of the resulting GPU implementation is compared to the one achieved with a traditional CPU implementation. The comparison is made using a minimal sensor network with bearings-only sensors. The resulting GPU filter which is the first complete GPU implementation of a PF published

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