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.

Adaptive Matrix Design for Boosting Compressed Sensing

Pareschi, Fabio;Setti, Gianluca
Ultimo
2018

Abstract

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.
Mangia, Mauro; Pareschi, Fabio; Rovatti, Riccardo; Setti, Gianluca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2379716
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