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|dc.contributor.author||Johnson, Ben A.||en|
|dc.identifier.citation||IEEE International Conference on Acoustics, Speech and Signal Processing, 15-20 May, 2007: pp.1129-1132||en|
|dc.description.abstract||For cases where the number of training samples T does not exceed the number of antenna elements M, we consider a detection-estimation problem for Gaussian sources occupying a low-rank m-dimensioned signal subspace within the associated covariance matrix (m < T < M). We derive a likelihood ratio that for the null hypothesis is described by a probability function that does not depend on a scenario, and investigate a (non-trivial) correspondence between the likelihood function and the derived likelihood ratio with respect to maximization performance. Practical application of this technique is illustrated for under-sampled (T < M) conditions for the purpose of MUSIC performance enhancement in the "threshold" region.||en|
|dc.description.statementofresponsibility||Johnson, B.A. and Abramovich, Y.L.||en|
|dc.rights||© 2008 IEEE – All Rights Reserved||en|
|dc.title||GLRT-based outlier prediction and cure in under-sampled training conditions using a singular likelihood ratio||en|
|dc.contributor.school||School of Electrical and Electronic Engineering||en|
|dc.contributor.conference||IEEE International Conference on Acoustics, Speech and Signal Processing (2007 : Honolulu, Hawaii)||en|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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