Title
Particle filtering: the need for speed
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.
Year
DOI
Venue
2010
10.1155/2010/181403
EURASIP J. Adv. Sig. Proc.
Keywords
Field
DocType
gpu implementation,cpu filter,parallel particle filter,traditional cpu implementation,complete gpu implementation,parallel architecture,gpgpu technique,particle filter,scalable implementation,parallel recursive bayesian estimation,control engineering
Computer science,Particle filter,Computational science,Artificial intelligence,Computer hardware,Computer vision,Central processing unit,Recursive Bayesian estimation,General-purpose computing on graphics processing units,Rendering (computer graphics),Graphics processing unit,Wireless sensor network,Scalability
Journal
Volume
Issue
ISSN
2010,
1
1687-6180
Citations 
PageRank 
References 
25
1.22
13
Authors
3
Name
Order
Citations
PageRank
Gustaf Hendeby121621.37
Rickard Karlsson21238.89
Fredrik Gustafsson32287281.33