The GM-PHD filter multiple target tracker

Daniel E. Clark, Kusha Panta, Ba N. Vo

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

97 Citations (Scopus)

Abstract

The Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD Filter) was proposed recently for jointly estimating the time-varying number of targets and their states from a noisy sequence of sets of measurements which may have missed detections and false alarms. The initial implementation of the GM-PHD filter provided estimates for the set of target states at each point in time but did not ensure continuity of the individual target tracks. It is shown here that the trajectories of the targets can be determined directly from the evolution of the Gaussian mixture and that single Gaussions within this mixture accurately track the correct targets. Furthermore, the technique is demonstrated to be successful in estimating the correct number of targets and their trajectories in high clutter density and shows better performance than the MHT filter.

Original languageEnglish
Title of host publication2006 9th International Conference on Information Fusion, FUSION
DOIs
Publication statusPublished - 2006
Event2006 9th International Conference on Information Fusion - Florence, Italy
Duration: 10 Jul 200613 Jul 2006

Conference

Conference2006 9th International Conference on Information Fusion
Abbreviated titleFUSION
CountryItaly
CityFlorence
Period10/07/0613/07/06

Keywords

  • Data association
  • Filtering
  • PHD filter
  • Random sets
  • Tracking

Fingerprint Dive into the research topics of 'The GM-PHD filter multiple target tracker'. Together they form a unique fingerprint.

Cite this