By Topic

A Parallel Resampling Algorithm for Particle Filtering on Shared-Memory Architectures

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Peng Gong ; Dept. of Electr. & Comput. Eng., Ohio State Univ., Columbus, OH, USA ; Yuksel Ozan Basciftci ; Fusun Ozguner

Many real-world applications such as positioning, navigation, and target tracking for autonomous vehicles require the estimation of some time-varying states based on noisy measurements made on the system. Particle filters can be used when the system model and the measurement model are not Gaussian or linear. However, the computational complexity of particle filters prevents them from being widely adopted. Parallel implementation will make particle filters more feasible for real-time applications. Effective resampling algorithms like the systematic resampling algorithm are serial. In this paper, we propose the shared-memory systematic resampling (SMSR) algorithm that is easily parallelizable on existing architectures. We verify the performance of SMSR on graphics processing units. Experimental results show that the proposed SMSR algorithm can achieve a significant speedup over the serial particle filter.

Published in:

Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International

Date of Conference:

21-25 May 2012