  We consider the problem of detecting the presence of a spatially correlated
multichannel signal corrupted by additive Gaussian noise (i.i.d across
sensors). No prior knowledge is assumed about the system parameters such as the
noise variance, number of sources and correlation among signals. It is well
known that the GLRT statistics for this composite hypothesis testing problem
are asymptotically optimal and sensitive to variation in system model or its
parameter. To address these shortcomings we present a few non-parametric
statistics which are functions of the elements of Bartlett decomposed sample
covariance matrix. They are designed such that the detection performance is
immune to the uncertainty in the knowledge of noise variance. The analysis
presented verifies the invariability of threshold value and identifies a few
specific scenarios where the proposed statistics have better performance
compared to GLRT statistics. The sensitivity of the statistic to correlation
among streams, number of sources and sample size at low signal to noise ratio
are discussed.
