Analog-to-Information Conversion via Random Demodulation

By Sami Kirolos, Jason N. Laska, Michael B. Wakin, Marco F. Duarte, Dror Baron, Tamer Ragheb, Y. Massoud, Richard Baraniuk. 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software, 2006

428 citations recorded by the source corpus.

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 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

Subfields: Biomedical Engineering, Computational Mechanics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/018cb94d8be21dc34ca744b211a2eded16e109c0

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Citations in the corpus

Compressive Sensing [Lecture Notes] (2007, background): “x(t) has only K degrees of freedom and thus can be measured at a much lower rate [8], [9].”
One sketch for all: fast algorithms for compressed sensing (2007, background): “Categories and Subject Descriptors G.4 [Mathematical Software]: Algorithm design and analysis Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial…”
Fixed-Point Continuation for l1-Minimization: Methodology and Convergence (2008, background): “Applications of compressed sensing include compressive imaging [56, 65, 66], medical imaging [42], multisensor and distributed compressed sensing [1], analog-to-information conversion [36, 37, 38, 59], and missing data recovery [67].”
Compressive sensing: From theory to applications, a survey (2013, neutral): “AIC is based on three main components: demodulation, filtering and uniform sampling [80], [81].”
Compressive Sensing (2008, background): “An example of a practical analog compressive sensing system — a so-called “analog-to-information” converter — is given in [7]; there are interesting connections to [8].”
Theory and Implementation of an Analog-to-Information Converter using Random Demodulation (2007, methodology): “Our design is based on an extension of the CS framework to analog signals, first described in [1].”
Compressive Sensing by Random Convolution (2009, methodology): “In the rst, we simply sample the convolution at a small number of locations, in the second we apply the \random demodulator" architecture of [ 22 , 40].”
Random Projections of Smooth Manifolds (2009, neutral): “Moreover, to support the developing CS theory, physical “analog-to-information” hardware devices [31, 42] and imaging systems [39] have been proposed that directly acquire random projections of signals into RM without first sampling the signals in RN ; this eliminates the computational burden of app”
Compressive sampling for accelerometer signals in structural health monitoring (2011, background): “Compressive sampling results for Haar wavelet basis We used the l1-magic package available online at http:// www.acm.caltech.edu/l1magic/ to solve the CS problem (P2) in Equation (17) The Haar wavelet basis is selected to form the orthogonal basis matrix ).”