Performance analysis of a track before detect dynamic programming algorithm via generalized Pareto distribution

Abstract

We analyze a dynamic programming (DP)-based track before detect (TBD) algorithm. By using the generalized Pareto distribution (GPD) in extreme value theory, we obtain explicit expressions for the performance measures of the algorithm such as probability of detection and false alarm. Our analysis has two advantages. First the unrealistic the distribution for data from the exponential class assumptions used in EVT are not required. Second, the probability of detection and false alarm curves obtained fit computer simulated performance results significantly more accurately than previously proposed analyses of the TBD algorithm. (4 pages)