  The performance of Bayesian detection of Gaussian signals using noisy
observations is investigated via the error exponent for the average error
probability. Under unknown signal correlation structure or limited processing
capability it is reasonable to use the simple quadratic detector that is
optimal in the case of an independent and identically distributed (i.i.d.)
signal. Using the large deviations principle, the performance of this detector
(which is suboptimal for non-i.i.d. signals) is compared with that of the
optimal detector for correlated signals via the asymptotic relative efficiency
defined as the ratio between sample sizes of two detectors required for the
same performance in the large-sample-size regime. The effects of SNR on the ARE
are investigated. It is shown that the asymptotic efficiency of the simple
quadratic detector relative to the optimal detector converges to one as the SNR
increases without bound for any bounded spectrum, and that the simple quadratic
detector performs as well as the optimal detector for a wide range of the
correlation values at high SNR.
