Analog to Information conversion is a new paradigm in signal digitalization. In this framework, compressed sensing theory allows to reconstruct sparse signal from a limited number of measures. In this work, we will assume that the signal is not only sparse but also localized in a given domain, so that its energy is concentrated in a subspace. We will present a formal and quantitative discussion to explain how localization of sparse signals can be exploited to improve the quality of the reconstructed signal.
Mauro Mangia, Riccardo Rovatti, Gianluca Setti (2011). Analog-to-information conversion of sparse and non-white signals: Statistical design of sensing waveforms. Piscataway, N.J. : IEEE [10.1109/ISCAS.2011.5938019].
Analog-to-information conversion of sparse and non-white signals: Statistical design of sensing waveforms
MANGIA, MAURO;ROVATTI, RICCARDO;
2011
Abstract
Analog to Information conversion is a new paradigm in signal digitalization. In this framework, compressed sensing theory allows to reconstruct sparse signal from a limited number of measures. In this work, we will assume that the signal is not only sparse but also localized in a given domain, so that its energy is concentrated in a subspace. We will present a formal and quantitative discussion to explain how localization of sparse signals can be exploited to improve the quality of the reconstructed signal.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
