Robust locally optimum detection of signals in dependent noise
K. Gerlach, K. James Sangston · IEEE Transactions on Information Theory · 1993
A robust locally optimum detector of a signal embedded in additive dependent nonGaussian noise is presented. The performance criterion is Bayes risk, the sample size is finite, and the uncertainty class of multivariate inputs is the in -contamination model. The locally optimum detector is shown to be a censored version of the nominal likelihood ratio.>