Data association for the PHD filter

Daniel E. Clark, Judith Bell

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

43 Citations (Scopus)

Abstract

The Probability Hypothesis Density (PHD) filter was developed as a suboptimal method for tracking a time varying number of targets. The first order statistical moment of the multiple target posterior distribution, called the Probability Hypothesis Density, gives the expected locations of the targets. This property is used instead of the full multi-target posterior distribution as it requires significantly less computation. Particle filter implementations have demonstrated the potential of the algorithm for real-time tracking applications. One of the main criticisms of the PHD filter is that there is no means of associating the same target between frames. Whilst this may be of advantage if the main concern is where the targets are, it is a major drawback if it is necessary to identify the trajectories of the different targets. Novel techniques for solving the problem of track continuity are presented here and demonstrated on simulated data. © 2005 IEEE.

Original languageEnglish
Title of host publicationProceedings of the 2005 Intelligent Sensors, Sensor Networks and Information Processing Conference
Pages217-222
Number of pages6
Volume2005
Publication statusPublished - 2005
Event2005 Intelligent Sensors, Sensor Networks and Information Processing Conference - Melbourne, Australia
Duration: 5 Dec 20058 Dec 2005

Conference

Conference2005 Intelligent Sensors, Sensor Networks and Information Processing Conference
CountryAustralia
CityMelbourne
Period5/12/058/12/05

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