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Particle Filtering: The Need for Speed
German Research Centre Artificial Intelligence, Germany.ORCID iD: 0000-0002-1971-4295
NIRA Dynamics AB, Sweden.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2010 (English)In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2010, no 181403Article in journal (Refereed) Published
Abstract [en]

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 to this date, is faster than the CPU filter when many particles are used, maintaining the same accuracy. The parallelization utilizes ideas that can be applicable for other applications.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2010. Vol. 2010, no 181403
Keyword [en]
Particle filter, Graphics processing unit, Central processing unit, Sensor network
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-58784DOI: 10.1155/2010/181403ISI: 000280785800001OAI: diva2:345774
Available from: 2010-08-27 Created: 2010-08-27 Last updated: 2015-09-22

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