Multichannel signal detection involving temporal and cross-channel correlation

This paper considers the Gaussian multichannel binary detection problem. A multichannel generalized likelihood ratio is derived using a model-based approach where the signal and nonwhite additive noise are characterized by autoregressive (AR) vector processes. Detection performance is obtained via the Monte Carlo procedure for the special case where the AR process parameters of the observation processes are assumed to be known. Specifically, detection performance is obtained for two-channel observation vectors containing various temporal and cross-channel correlation on both the signal and nonwhite additive noise. The computed results are compared with known optimal detection curves using coherent (both in time and across channels) as well as noncoherent integration. In addition, the degradation in performance due to a low signal-to-noise ratio on a specific channel is also considered