In this paper we take into account the rakeness approach in the design of Compressed Sensing (CS) based system, which allows, by means of the matching of some statistical properties of the CS sampling functions with statistical features of the input signal, to greatly increase system performance in terms of either a reduction of resources (hardware, energy, etc) required for the signal acquisition or an increase in the acquisition quality. In particular, with respect to the general formulation, we make two additional and non-restrictive hypotheses to ensure a good behavior of the system. With these, we can compute an upper and a lower bound for the parameter r used to control the statistical matching level, and we show with some numerical examples that the choice of r is not critical. In particular, any r value taken from the computed interval ensures almost optimal performance, making the rakeness approach robust and worthwhile.

Correlation tuning in compressive sensing based on rakeness: A case study / Bertoni, Nicola; Mangia, Mauro; Pareschi, Fabio; Rovatti, Riccardo; Setti, Gianluca. - STAMPA. - (2013), pp. 257-260. ((Intervento presentato al convegno 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013 tenutosi a Abu Dhabi nel December 8-11, 2013 [10.1109/ICECS.2013.6815403].

Correlation tuning in compressive sensing based on rakeness: A case study

Fabio Pareschi;Gianluca Setti
2013

Abstract

In this paper we take into account the rakeness approach in the design of Compressed Sensing (CS) based system, which allows, by means of the matching of some statistical properties of the CS sampling functions with statistical features of the input signal, to greatly increase system performance in terms of either a reduction of resources (hardware, energy, etc) required for the signal acquisition or an increase in the acquisition quality. In particular, with respect to the general formulation, we make two additional and non-restrictive hypotheses to ensure a good behavior of the system. With these, we can compute an upper and a lower bound for the parameter r used to control the statistical matching level, and we show with some numerical examples that the choice of r is not critical. In particular, any r value taken from the computed interval ensures almost optimal performance, making the rakeness approach robust and worthwhile.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2696682