Detection of sparse random signals using compressive measurements
2012 (English)In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, IEEE , 2012, 3257-3260 p.Conference paper (Refereed)
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
binary hypothesis, Compressive sensing, receiver operating characteristic, signal detection, sparse Gaussian vector
IdentifiersURN: urn:nbn:se:kth:diva-104965DOI: 10.1109/ICASSP.2012.6288610ScopusID: 2-s2.0-84867609293ISBN: 978-146730046-9OAI: oai:DiVA.org:kth-104965DiVA: diva2:570415
2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012, 25 March 2012 through 30 March 2012, Kyoto
FunderICT - The Next Generation
QC 201211192012-11-192012-11-142013-04-15Bibliographically approved