Analog-to-Information Conversion via Random Demodulation
Abstract
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
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Donoho, D. L. (2004) · Cited by 16,703 · see citing works
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639 · see citing works
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Tropp, J. A., & Gilbert, A. C. (2007) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.909108 · Cited by 9,828 · see citing works
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827596304010 · Cited by 6,734 · see citing works
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001) · SIAM Review · doi:10.1137/s003614450037906x · Cited by 5,030 · see citing works
Sampling signals with finite rate of innovation
Vetterli, M., Marziliano, P., & Blu, T. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.1003065 · Cited by 1,071 · see citing works
Error correction via linear programming
Candès, E. J., Rudelson, M., Tao, T., & Vershynin, R. (2005) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2005.5464411 · Cited by 251 · see citing works
Candès, E. J., Romberg, J. K., & Tao, T. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.math/0409186 · Cited by 64 · see citing works
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Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
Tropp, J. A., Laska, J. N., Duarte, M. F., Romberg, J. K., & Baraniuk, R. G. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2034811 · Cited by 1,089 · see citing works
Self-citationYu, Y., Han, F., Bao, Y., & Ou, J. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2512846 · Cited by 48 · see citing works
Emerging data technology in structural health monitoring: compressive sensing technology
Bao, Y., Li, H., & Ou, J. (2014) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-013-0064-1 · Cited by 43 · see citing works
Liu, X., Li, T., Fan, X., & Chen, Z. (2019) · Sensors · doi:10.3390/s19061477 · Cited by 8 · see citing works
- Authors
- Kirolos, S., Laska, J., Wakin, M., Duarte, M., Baron, D., Ragheb, T., Massoud, Y., & Baraniuk, R.
- Affiliations
- Rice University (US)
- Venue
- 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software
- Publisher
- IEEE
- Published
- 2006-10
- Pages
- 71-74
- OpenAlex
- W2533057217
- Cited by
- 393
- References
- 8
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 5 · 2023: 11 · 2022: 12 · 2021: 13 · 2020: 16 · 2019: 21 · 2018: 22 · 2017: 28 · 2016: 28 · 2015: 40 · 2014: 34 · 2013: 33 · 2012: 32
- FWCI
- 30.98
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Dallas Circuits and Systems Conference (DCAS) (publishedVersion)
- Funders
- National Science Foundation
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Keywords
- analog-to-information conversion, compressed sensing, sparse signal recovery, sampling theorem
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