The optimality of the censored mean-level detector

Abstract

It is shown that the CMLD (censored mean-level detector) is optimal in that the probability of detection is maximized for a given probability of false alarm when no contaminating signals are present in the reference cells. Since the probability of detection in this case is the same as the probability of detection for the MLD (mean-level detector) when k reference cells are used, and since the probability of detection of the MLD converges to the probability of detection of a fixed-threshold Neyman-Pearson test as the number of reference cells becomes large, then this is also true of the CMLD as k becomes large. >