Multi-target state estimation and track continuity for the particle PHD filter

Daniel E. Clark, Judith Bell

Research output: Contribution to journalArticlepeer-review

132 Citations (Scopus)


Particle filter approaches for approximating the first-order moment of a joint multi-target probability distribution, or probability hypothesis density (PHD), have demonstrated a feasible suboptimal method for tracking a time-varying number of targets in real-time. We consider two techniques for estimating the target states at each iteration, namely k-means clustering and mixture modelling via the expectation-maximization (EM) algorithm. We present novel techniques for associating the targets between frames to enable track continuity. © 2007 IEEE.

Original languageEnglish
Pages (from-to)1441-1453
Number of pages13
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number4
Publication statusPublished - Oct 2007


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