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.
|Number of pages||13|
|Journal||IEEE Transactions on Aerospace and Electronic Systems|
|Publication status||Published - Oct 2007|