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Detection of sparse random signals using compressive measurements
KTH, School of Electrical Engineering (EES), Communication Theory.ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0003-2298-6774
2012 (English)In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, IEEE , 2012, 3257-3260 p.Conference paper (Refereed)
Abstract [en]

We consider the problem of detecting a sparse random signal from the compressive measurements without reconstructing the signal. Using a subspace model for the sparse signal where the signal parameters are drawn according to Gaussian law, we obtain the detector based on Neyman-Pearson criterion and analytically determine its operating characteristics when the signal covariance is known. These results are extended to situations where the covariance cannot be estimated. The results can be used to determine the number of measurements needed for a particular detector performance and also illustrate the presence of an optimal support for a given number of measurements.

Place, publisher, year, edition, pages
IEEE , 2012. 3257-3260 p.
, IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
Keyword [en]
binary hypothesis, Compressive sensing, receiver operating characteristic, signal detection, sparse Gaussian vector
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-104965DOI: 10.1109/ICASSP.2012.6288610ScopusID: 2-s2.0-84867609293ISBN: 978-146730046-9OAI: diva2:570415
2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012, 25 March 2012 through 30 March 2012, Kyoto
ICT - The Next Generation

QC 20121119

Available from: 2012-11-19 Created: 2012-11-14 Last updated: 2013-04-15Bibliographically approved

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