Title :
Robust locally optimum detection of signals in dependent noise
Author :
Gerlach, Karl ; Sangston, Kevin J.
Author_Institution :
US Naval Res. Lab., Washington, DC, USA
fDate :
5/1/1993 12:00:00 AM
Abstract :
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 ∈-contamination model. The locally optimum detector is shown to be a censored version of the nominal likelihood ratio
Keywords :
Bayes methods; noise; signal detection; ∈-contamination model; Bayes risk; additive dependent nonGaussian noise; multivariate inputs; nominal likelihood ratio; robust locally optimum detector; signal detection; uncertainty class; Additive noise; Costs; Density measurement; Detectors; Noise robustness; Particle measurements; Signal analysis; Signal detection; Size measurement; Testing; Uncertainty;
Journal_Title :
Information Theory, IEEE Transactions on
