Convergence Performance of Adaptive Detectors. Part 2

Abstract

Abstract : Two schemes for adaptive detection are compared: Kelly's generalized likelihood ratio test (GLRT) and the mean level adaptive detector (MLAD). Detection performance P(D) is predicted for the two schemes under the assumptions that the input noises are zero-mean Gaussian random variables that are temporally independent but spatially correlated; and the desired signal is Rayleight distributed. P(D) is computed as a function of the false alarm probability, the number of input channels, the number of independent samples- per-channel, and the matched filtered output signal-to-noise (S/N) power ratio. In this analysis, the GLRT is shown to have better detection performance than the MLAD. The difference in detection performance increases as one uses fewer input samples. However, the required number of samples necessary to have only a 3 dB detection loss for both detection schemes is approximately the same. This is significant since, for the present, the MLAD is considerably less complex to implement than the GLRT.