Detection and estimation with compressive measurements

dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.authorDavenport, Mark A.en_US
dc.contributor.authorWakin, Michael B.en_US
dc.date.accessioned2008-08-07T14:23:55Zen_US
dc.date.available2008-08-07T14:23:55Zen_US
dc.date.issued2006-11-01en_US
dc.description.abstractThe recently introduced theory of compressed sensing enables the reconstruction of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist rate samples. Interestingly, it has been shown that random projections are a satisfactory measurement scheme. This has inspired the design of physical systems that directly implement similar measurement schemes. However, despite the intense focus on the reconstruction of signals, many (if not most) signal processing problems do not require a full reconstruction of the signal { we are often interested only in solving some sort of detection problem or in the estimation of some function of the data. In this report, we show that the compressed sensing framework is useful for a wide range of statistical inference tasks. In particular, we demonstrate how to solve a variety of signal detection and estimation problems given the measurements without ever reconstructing the signals themselves. We provide theoretical bounds along with experimental results.en_US
dc.description.sponsorshipONR grants N00014-06-1-0769 and N00014-06-1-0829; AFOSR grant FA9550- 04-0148; DARPA grants N66001-06-1-2011 and N00014-06-1-0610; NSF grants CCF-0431150, CNS-0435425, and CNS-0520280; and the Texas Instruments Leadership University Program.en_US
dc.identifier.citationR. G. Baraniuk, M. A. Davenport and M. B. Wakin, "Detection and estimation with compressive measurements," 2006.en_US
dc.identifier.urihttps://hdl.handle.net/1911/21677en_US
dc.language.isoengen_US
dc.relation.IsPartOfSeriesRice University ECE Technical Report;TREE 0610en_US
dc.subjectcompressive sensingen_US
dc.subjectdetectionen_US
dc.subjectestimationen_US
dc.titleDetection and estimation with compressive measurementsen_US
dc.typeReporten_US
dc.type.dcmiTexten_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
decm.pdf
Size:
265.79 KB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.69 KB
Format:
Item-specific license agreed upon to submission
Description:
Collections