Data association and track management for the gaussian mixture probability hypothesis density filter

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

Research output: Contribution to journalArticle

191 Citations (Scopus)

Abstract

The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own. © 2006 IEEE.

Original languageEnglish
Article number5259179
Pages (from-to)1003-1016
Number of pages14
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume45
Issue number3
DOIs
Publication statusPublished - Jul 2009

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