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dc.contributor.authorJohnson, Ben A.en
dc.contributor.authorAbramovich, Yurien
dc.identifier.citationIEEE International Conference on Acoustics, Speech and Signal Processing, 15-20 May, 2007: pp.1129-1132en
dc.description.abstractFor 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.statementofresponsibilityJohnson, B.A. and Abramovich, Y.L.en
dc.rights© 2008 IEEE – All Rights Reserveden
dc.titleGLRT-based outlier prediction and cure in under-sampled training conditions using a singular likelihood ratioen
dc.typeConference paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conferenceIEEE International Conference on Acoustics, Speech and Signal Processing (2007 : Honolulu, Hawaii)en
dc.contributor.conferenceICASSP 2007en
Appears in Collections:Electrical and Electronic Engineering publications

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