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Open Access

Particle Filtering: The Need for Speed

  • Gustaf Hendeby1,
  • Rickard Karlsson2Email author and
  • Fredrik Gustafsson (EURASIP Member)3
EURASIP Journal on Advances in Signal Processing20102010:181403

https://doi.org/10.1155/2010/181403

Received: 22 February 2010

Accepted: 26 May 2010

Published: 21 June 2010

Abstract

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.

Keywords

Sensor NetworkGraphic Processing UnitParticle FilterEmbed SystemCentral Processing Unit

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
Department of Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslatern, Germany
(2)
NIRA Dynamics AB, Linköping, Sweden
(3)
Department of Electrical Engineering, Linköping University, Linköping, Sweden

Copyright

© Gustaf Hendeby et al. 2010

This article is published under license to BioMed Central Ltd. 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.

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