Measurement Combining and Progressive Reconstruction in Compressive Sensing

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Measurement Combining and Progressive Reconstruction in Compressive Sensing

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dc.contributor.author Chen, Kevin
dc.contributor.author Kung, H.T. T.
dc.contributor.author Vlah, Dario
dc.contributor.author Suter, Bruce
dc.date.accessioned 2012-12-09T19:18:39Z
dc.date.issued 2011
dc.identifier.citation Chen, 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.isbn 9781467300797 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:10021422
dc.description.abstract Compressive 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.sponsorship Engineering and Applied Sciences en_US
dc.language.iso en_US en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.relation.isversionof doi:10.1109/MILCOM.2011.6127545 en_US
dc.relation.hasversion http://www.eecs.harvard.edu/~htk/publication/2011-milcom-chen-kung-vlah-suter.pdf en_US
dash.license OAP
dc.subject compressed sensing en_US
dc.subject decoding en_US
dc.subject dictionaries en_US
dc.subject encoding en_US
dc.subject image-reconstruction en_US
dc.subject interference en_US
dc.subject vectors en_US
dc.subject object detection en_US
dc.subject signal detection en_US
dc.subject signal reconstruction en_US
dc.subject compressive measurements en_US
dc.subject compressive sensing en_US
dc.subject distributed sensor systems en_US
dc.subject progressive reconstruction en_US
dc.subject signal acquisition en_US
dc.subject source separation en_US
dc.subject sparse signal en_US
dc.subject target detection en_US
dc.subject sub-Nyquist rate en_US
dc.title Measurement Combining and Progressive Reconstruction in Compressive Sensing en_US
dc.type Monograph or Book en_US
dc.description.version Accepted Manuscript en_US
dash.depositing.author Kung, H.T. T.
dc.date.available 2012-12-09T19:18:39Z

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  • FAS Scholarly Articles [6885]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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