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  • Research Article
  • Open Access

Particle Filtering: The Need for Speed

  • 1,
  • 2Email author and
  • 3
EURASIP Journal on Advances in Signal Processing20102010:181403

  • Received: 22 February 2010
  • Accepted: 26 May 2010
  • Published:


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.


  • Sensor Network
  • Graphic Processing Unit
  • Particle Filter
  • Embed System
  • Central Processing Unit

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Authors’ Affiliations

Department of Augmented Vision, German Research Center for Artificial Intelligence, 67663 Kaiserslatern, Germany
NIRA Dynamics AB, Teknikringen 6, 58330 Linköping, Sweden
Department of Electrical Engineering, Linköping University, 58183 Linköping, Sweden


© 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.