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https://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 |
DOI: | 10.1109/ICASSP.2007.366439 |
Appears in Collections: | Electrical and Electronic Engineering publications |
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