TY - GEN
T1 - Analog-to-information conversion via random demodulation
AU - Kirolos, Sami
AU - Laska, Jason
AU - Wakin, Michael
AU - Duarte, Marco
AU - Baron, Dror
AU - Ragheb, Tamer
AU - Massoud, Yehia
AU - Baraniuk, Richard
PY - 2006
Y1 - 2006
N2 - Many problems in radar and communication signal processing involve radio frequency (RF) signals of very high bandwidth. This presents a serious challenge to systems that might attempt to use a high-rate analog-to-digital converter (ADC) to sample these signals, as prescribed by the Shannon/Nyquist sampling theorem. In these situations, however, the information level of the signal is often far lower than the actual bandwidth, which prompts the question of whether more efficient schemes can be developed for measuring such signals. In this paper we propose a system that uses modulation, filtering, and sampling to produce a low-rate set of digital measurements. Our "analog-to-information converter" (AIC) is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal having a sparse representation in some dictionary can be recovered from a small number of linear projections of that signal. We generalize the CS theory to continuous-time sparse signals, explain our proposed AIC system in the CS context, and discuss practical issues regarding implementation.
AB - Many problems in radar and communication signal processing involve radio frequency (RF) signals of very high bandwidth. This presents a serious challenge to systems that might attempt to use a high-rate analog-to-digital converter (ADC) to sample these signals, as prescribed by the Shannon/Nyquist sampling theorem. In these situations, however, the information level of the signal is often far lower than the actual bandwidth, which prompts the question of whether more efficient schemes can be developed for measuring such signals. In this paper we propose a system that uses modulation, filtering, and sampling to produce a low-rate set of digital measurements. Our "analog-to-information converter" (AIC) is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal having a sparse representation in some dictionary can be recovered from a small number of linear projections of that signal. We generalize the CS theory to continuous-time sparse signals, explain our proposed AIC system in the CS context, and discuss practical issues regarding implementation.
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U2 - 10.1109/DCAS.2006.321036
DO - 10.1109/DCAS.2006.321036
M3 - Conference contribution
AN - SCOPUS:46249096592
SN - 1424406692
SN - 9781424406692
T3 - 2006 IEEE Dallas/CAS Workshop onDesign, Applications, Integration and Software, DCAS-06
SP - 71
EP - 74
BT - 2006 IEEE Dallas ICAS Workshop on Design, Applications, Integration and Software, DCAS-06
T2 - 2006 IEEE Dallas ICAS Workshop on Design, Applications, Integration and Software, DCAS-06
Y2 - 29 October 2006 through 30 October 2006
ER -