Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||GLRT-based outlier prediction and cure in under-sampled training conditions using a singular likelihood ratio|
|Author:||Johnson, Ben A.|
|Citation:||IEEE International Conference on Acoustics, Speech and Signal Processing, 15-20 May, 2007: pp.1129-1132|
|Conference Name:||IEEE International Conference on Acoustics, Speech and Signal Processing (2007 : Honolulu, Hawaii)|
|School/Discipline:||School of Electrical and Electronic Engineering|
|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|
|Appears in Collections:||Electrical and Electronic Engineering publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.