Multiple-Channel Detection of a Gaussian Time Series Over Frequency-Flat Channels
This paper addresses the problem of deciding whether a set of realizations of a vector-valued time series with unknown temporal correlation are spatially correlated or not. Specifically, the spatial correlation is induced by a colored source over a frequency-flat Single-Input Multiple-Output (SIMO) channel distorted by independent and identically distributed noises with temporal correlation. The Generalized Likelihood Ratio Test (GLRT) for this detection problem does not have a closed-form expression and the authors have to resort to numerical optimization techniques. In particular, they apply the successive convex approximations approach which relies on solving a series of convex problems that approximate the original (non-convex) one.