Accelerating the single cluster PHD filter with a GPU implementation

Chee Sing Lee, Jose Franco, Jeremie Houssineau, Daniel Clark

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The SC-PHD filter is an algorithm which was designed to solve a class of multiple object estimation problems where it is necessary to estimate the state of a single-target parent process, in addition to estimating the state of a multi-object population which is conditioned on it. The filtering process usually employs a number of particles to represent the parent process, coupled each with a conditional PHD filter, which is computationally burdensome. In this article, an implementation is described which exploits the parallel nature of the filter to obtain considerable speed-up with the help of a GPU. Several considerations need to be taken into account to make efficient use of the GPU, and these are also described here.

Original languageEnglish
Title of host publication2014 International Conference on Control, Automation and Information Sciences, ICCAIS 2014
PublisherIEEE
Pages53-58
Number of pages6
ISBN (Print)9781479972043
DOIs
Publication statusPublished - 2014
Event3rd International Conference on Control, Automation and Information Sciences - Gwangju, United Kingdom
Duration: 2 Dec 20145 Dec 2014

Conference

Conference3rd International Conference on Control, Automation and Information Sciences
Abbreviated titleICCAIS 2014
CountryUnited Kingdom
CityGwangju
Period2/12/145/12/14

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