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In situ compressive sensing

Publication ,  Journal Article
Carin, L; Liu, D; Xue, Y
Published in: IEEE Workshop on Statistical Signal Processing Proceedings
December 1, 2007

Compressive sensing (CS) is a framework that exploits the compressible character of most natural signals, allowing the accurate measurement of an m-dimensional real signal u in terms of n≪m real measurements v. The CS measurements may be represented in terms of an n x m matrix that defines the linear relationship between v and u. In this paper we demonstrate that similar linear mappings of the form u → v are manifested naturally by wave propagation in complex media, and therefore in situ CS measurements may be performed simply by exploiting the complex propagation and scattering properties of natural environments. A similar phenomenon is observed in time-reversal imaging, to which connections are made. In addition to presenting the basic in situ CS framework, a simple but practical example problem is considered. ©2007 IEEE.

Duke Scholars

Published In

IEEE Workshop on Statistical Signal Processing Proceedings

DOI

Publication Date

December 1, 2007

Start / End Page

322 / 325
 

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Carin, L., Liu, D., & Xue, Y. (2007). In situ compressive sensing. IEEE Workshop on Statistical Signal Processing Proceedings, 322–325. https://doi.org/10.1109/SSP.2007.4301272
Carin, L., D. Liu, and Y. Xue. “In situ compressive sensing.” IEEE Workshop on Statistical Signal Processing Proceedings, December 1, 2007, 322–25. https://doi.org/10.1109/SSP.2007.4301272.
Carin L, Liu D, Xue Y. In situ compressive sensing. IEEE Workshop on Statistical Signal Processing Proceedings. 2007 Dec 1;322–5.
Carin, L., et al. “In situ compressive sensing.” IEEE Workshop on Statistical Signal Processing Proceedings, Dec. 2007, pp. 322–25. Scopus, doi:10.1109/SSP.2007.4301272.
Carin L, Liu D, Xue Y. In situ compressive sensing. IEEE Workshop on Statistical Signal Processing Proceedings. 2007 Dec 1;322–325.

Published In

IEEE Workshop on Statistical Signal Processing Proceedings

DOI

Publication Date

December 1, 2007

Start / End Page

322 / 325