Integrated particle filter for target tracking in clutter

In a typical surveillance situation the number and the trajectories of targets are a priori unknown. Each measurement has an unknown source; either clutter, a target being tracked or a new target. The tracks are initialised and updated using measurements, thus both true and false tracks exist at any given time. The authors present a particle filter approach which recursively calculates the probability of target existence, which may be used as a track quality measure for the false track discrimination. Single target, joint multitarget and linear multitarget versions are presented. The algorithms assume manoeuvring (multiple trajectory propagation models) targets and state dependent probability of target detection.