- Research Article
- Open Access
Particle Filtering: The Need for Speed
EURASIP Journal on Advances in Signal Processing volume 2010, Article number: 181403 (2010)
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.
To access the full article, please see PDF.
About this article
Cite this article
Hendeby, G., Karlsson, R. & Gustafsson (EURASIP Member), F. Particle Filtering: The Need for Speed. EURASIP J. Adv. Signal Process. 2010, 181403 (2010). https://doi.org/10.1155/2010/181403
- Sensor Network
- Graphic Processing Unit
- Particle Filter
- Embed System
- Central Processing Unit