Abstract
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 language | English |
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Pages (from-to) | 1441-1453 |
Number of pages | 13 |
Journal | IEEE Transactions on Aerospace and Electronic Systems |
Volume | 43 |
Issue number | 4 |
DOIs | |
Publication status | Published - Oct 2007 |