The University of British Columbia

We analyze a dynamic programming (DP)-based track before detect (TBD) algorithm. By using extreme value theory we obtain explicit expressions for various performance measures of the algorithm such as probability of detection and false alarm. Our analysis has two advantages. First the unrealistic Gaussian and independence assumptions used in previous works 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