Compressive sensing of localized signals: Application to analog-to-information conversion

Juri Ranieri, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


Compressed sensing hinges on the sparsity of signals to allow their reconstruction starting from a limited number of measures. When reconstruction is possible, the SNR of the reconstructed signal depends on the energy collected in the acquisition. Hence, if the sparse signal to be acquired is known to concentrate its energy along a known subspace, an additional "rakeness" criterion arises for the design and optimization of the measurement basis. Formal and qualitative discussion of such a criterion is reported within the framework of a well-known Analog-to-Information conversion architecture and for signals localized in the frequency domain. Non-negligible inprovements are shown by simulation. ©2010 IEEE.
Original languageEnglish (US)
Title of host publicationISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems
Number of pages4
StatePublished - Aug 31 2010
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-02-15


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