Track Score and Target Existence

Automatic target tracking in clutter initiates and updates both true tracks and false tracks. True tracks follow targets, and false tracks do not. False track discrimination is the procedure which confirms (vast majority of) true tracks, and terminates (vast majority of) false tracks. False track discrimination requires a measure of track quality to distinguish between true tracks and false tracks in a statistical sense. This paper compares two powerful track quality measures; the track score, as used in multi hypothesis tracking (MHT) and the probability of target existence, as used in integrated track splitting (ITS) filter. Both theoretical and simulation comparisons are presented in a single target tracking situation