Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/44882
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Type: Conference paper
Title: GLRT-based outlier prediction and cure in under-sampled training conditions using a singular likelihood ratio
Author: Johnson, Ben A.
Abramovich, Yuri
Citation: IEEE International Conference on Acoustics, Speech and Signal Processing, 15-20 May, 2007: pp.1129-1132
Publisher: IEEE
Issue Date: 2007
ISBN: 1424407281
Conference Name: IEEE International Conference on Acoustics, Speech and Signal Processing (2007 : Honolulu, Hawaii)
ICASSP 2007
School/Discipline: School of Electrical and Electronic Engineering
Statement of
Responsibility: 
Johnson, B.A. and Abramovich, Y.L.
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.
Rights: © 2008 IEEE – All Rights Reserved
RMID: 0020076103
DOI: 10.1109/ICASSP.2007.366439
Appears in Collections:Electrical and Electronic Engineering publications

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