Adaptive Matrix Design for Boosting Compressed Sensing

Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Compressed sensing (CS) has been proposed to reduce operating cost (e.g., energy requirements) of acquisition devices by leveraging its capability of sampling and compressing an input signal at the same time. This paper aims at increasing CS performance (i.e., either achieving a better compression or allowing a higher signal reconstruction quality) and proposes two novel methods. Our first approach (Nearly Orthogonal CS) is based on a geometric constraint enforcing diversity between compressed measurements, while the second one (Maximum-Energy CS) on a heuristic screening of candidate measurements that acts as a run-time self-adapted optimization technique. Intensive simulation results show that the proposed approaches have different applications, and ensure an appreciable performance boost with respect to the state-of-the-art.
Original languageEnglish (US)
Pages (from-to)1016-1027
Number of pages12
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number3
StatePublished - Mar 1 2018
Externally publishedYes

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


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