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dc.contributor.authorChen, Kevin
dc.contributor.authorKung, H. T.
dc.contributor.authorVlah, Dario
dc.contributor.authorSuter, Bruce
dc.date.accessioned2012-12-09T19:18:39Z
dc.date.issued2011
dc.identifier.citationChen, Hsieh-Chung, H.T. Kung, Dario Vlah, and Bruce Suter. 2011. Measurement combining and progressive reconstruction in compressive sensing. In Proceedings of Military Communications Conference (MILCOM 2011), 163-168. Piscataway, New Jersey: IEEE Communications Society.en_US
dc.identifier.isbn9781467300797en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10021422
dc.description.abstractCompressive sensing has emerged as an important new technique in signal acquisition due to the surprising property that a sparse signal can be captured from measurements obtained at a sub-Nyquist rate. The decoding cost of compressive sensing, however, grows superlinearly with the problem size. In distributed sensor systems, the aggregate amount of compressive measurements encoded by the sensors can be substantial, and the decode cost for all the variables involved can be large. In this paper we propose a method to combine measurements from distributed sensors. With our method we can transport and store a single combined measurement set, rather than multiple sets for all sensors. We show that via source separation and joint decoding, it is possible to recover an approximate to the original signal from combined measurements using progressive reconstruction which focuses on individual sensors. This results in a reduction in the number of variables used in decoding and consequently a reduced decoding time. We show that the computed approximation to the signal can still have sufficient accuracy for target detection. We describe the combining approach and the associated progressive reconstruction, and we illustrate them with image recovery for simple target detection examples.en_US
dc.description.sponsorshipEngineering and Applied Sciencesen_US
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofdoi:10.1109/MILCOM.2011.6127545en_US
dc.relation.hasversionhttp://www.eecs.harvard.edu/~htk/publication/2011-milcom-chen-kung-vlah-suter.pdfen_US
dash.licenseOAP
dc.subjectcompressed sensingen_US
dc.subjectdecodingen_US
dc.subjectdictionariesen_US
dc.subjectencodingen_US
dc.subjectimage-reconstructionen_US
dc.subjectinterferenceen_US
dc.subjectvectorsen_US
dc.subjectobject detectionen_US
dc.subjectsignal detectionen_US
dc.subjectsignal reconstructionen_US
dc.subjectcompressive measurementsen_US
dc.subjectcompressive sensingen_US
dc.subjectdistributed sensor systemsen_US
dc.subjectprogressive reconstructionen_US
dc.subjectsignal acquisitionen_US
dc.subjectsource separationen_US
dc.subjectsparse signalen_US
dc.subjecttarget detectionen_US
dc.subjectsub-Nyquist rateen_US
dc.titleMeasurement Combining and Progressive Reconstruction in Compressive Sensingen_US
dc.typeMonograph or Booken_US
dc.description.versionAccepted Manuscripten_US
dash.depositing.authorKung, H.T. T.
dc.date.available2012-12-09T19:18:39Z
dc.identifier.doi10.1109/MILCOM.2011.6127545*
dash.contributor.affiliatedChen, Hsieh-Chung
dash.contributor.affiliatedKung, H.


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