Multi-target state estimation and track continuity for the particle PHD filter
Daniel E. Clark, James S. Bell · IEEE Transactions on Aerospace and Electronic Systems · 2007
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.