Comparative performance evaluation of GM-PHD filter in clutter

Radford Juang, Philippe Burlina

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

Abstract

Random Finite Sets (RFS) offer a diligent formalism for tracking an unknown number of targets with multiple sensors. The Probability Hypothesis Density (PHD) filter, and its Gaussian Mixture (GM) and Sequential Monte Carlo (SMC) implementations, provide tractable Bayesian Filtering methods that propagate the first order moment of the RFS probability density. A feature of the PHD filters is that they do not require association to complete their correction step. This, we believe, should constitute a significant advantage, especially in scenarios of high false alarm rates and track intersections, which can easily compromise most observerpredictor methods that must perform association to carry out their correction step. To test this hypothesis, we compare the performance of the GM-PHD to the traditional Kalman (KF) and SMC filters for visual tracking of multiple targets in moderate to heavy false alarm rate scenarios. Our tracking and association performance results seem to support this hypothesis.

Original languageEnglish (US)
Title of host publication2009 12th International Conference on Information Fusion, FUSION 2009
Pages1195-1202
Number of pages8
StatePublished - Nov 18 2009
Event2009 12th International Conference on Information Fusion, FUSION 2009 - Seattle, WA, United States
Duration: Jul 6 2009Jul 9 2009

Publication series

Name2009 12th International Conference on Information Fusion, FUSION 2009

Other

Other2009 12th International Conference on Information Fusion, FUSION 2009
CountryUnited States
CitySeattle, WA
Period7/6/097/9/09

Keywords

  • High false alarm rate
  • PHD filtering

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Information Systems
  • Software

Fingerprint Dive into the research topics of 'Comparative performance evaluation of GM-PHD filter in clutter'. Together they form a unique fingerprint.

Cite this