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dc.contributor.authorBaraniuk, Richard G.
Davenport, Mark A.
Wakin, Michael B.
dc.date.accessioned 2008-08-07T14:23:55Z
dc.date.available 2008-08-07T14:23:55Z
dc.date.issued 2006-11-01
dc.identifier.urihttps://hdl.handle.net/1911/21677
dc.description.abstract The 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.
dc.description.sponsorship ONR 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.
dc.language.iso en_US
dc.relation.IsPartOfSeries Rice University ECE Technical Report;TREE 0610
dc.subjectcompressive sensing
detection
estimation
dc.title Detection and estimation with compressive measurements
dc.type Report
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.citation R. G. Baraniuk, M. A. Davenport and M. B. Wakin, "Detection and estimation with compressive measurements," 2006.


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.

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