Statistical performance analysis of track initiation techniques

This paper analyzes the statistical performance of track initiation methods. It evaluates, by considering a track initiation as a problem of signal detection, the track detection probabilities and false track probabilities of four popular approaches: the rule-based method, the logic-based method, the Hough transform, and the modified Hough transform techniques. The first two methods detect the existence of tracks in a sequential fashion, and the fast two are image processing techniques that initiate tracks using a batch of frames. The analytical expressions for the track detection probability and false track probability of the four methods are derived to predict their capabilities in capturing new tracks and in rejecting false tracks due to false alarms and clutter background. Monte Carlo simulations are performed to confirm the analysis. It is observed that when the false track probability is held fixed, the logic-based and the modified Hough transform techniques have good detection performances.