Particle filter approaches for approximating the first-order moment of a joint, 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.
Multi-target state estimation and track continuity for the particle PHD filter
IEEE Transactions on Aerospace and Electronic Systems ; 43 , 4 ; 1441-1453
01.10.2007
1860437 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Multi-Target State Estimation and Track Continuity for the Particle PHD Filter
Online Contents | 2007
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