Cited by 76Sparse and Compressive Sensing TechniquesRadar Systems and Signal ProcessingMathematical Analysis and Transform Methods

A Nyquist folding analog-to-information receiver

Fudge, Gerald L., Bland, Ross E., Chivers, Mark A., Ravindran, Sujit, Haupt, Jarvis, Pace, P. E.·2008·2008 42nd Asilomar Conference on Signals, Systems and Computers

Abstract

Many radar and communications applications require detection and estimation of signal information across an extremely wide radio frequency (RF) bandwidth. In practice, however, direct digitization of this broadband RF environment is problematic. Physical limitations in analog-to-digital converter (ADC) technology restrict the total bandwidth that can be digitized, as well as the ability to digitize high RF signals directly. This paper describes a novel ldquoanalog-to-informationrdquo receiver, motivated by recent developments in compressed sensing (CS), which overcomes both of these challenges in certain settings. The proposed receiver performs frequency modulated pulsed sampling at sub-Nyquist/Shannon rates to compress a broadband RF environment into an analog interpolation filter. The RF sample clock modulation induces a Nyquist-zone dependent frequency modulation on the received signals, allowing separation and recovery of the signal information from a sparse broadband RF environment.

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Crossref · 10.1109/acssc.2008.5074464

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Authors
Fudge, G. L., Bland, R. E., Chivers, M. A., Ravindran, S., Haupt, J., & Pace, P. E.
Affiliations
  • University of Wisconsin–Madison (US)
  • Naval Postgraduate School (US)
Venue
2008 42nd Asilomar Conference on Signals, Systems and Computers
Publisher
IEEE
Published
2008-10
Pages
541-545
ISSN
1058-6393 (ISSN-L)
OpenAlex
W2171737517
Cited by
76
References
1
Citations by year
2026: 4 · 2025: 2 · 2024: 3 · 2023: 4 · 2022: 8 · 2021: 3 · 2020: 1 · 2019: 3 · 2018: 6 · 2017: 8 · 2016: 4 · 2015: 2 · 2014: 2 · 2013: 4 · 2012: 7
FWCI
9.91
Type
conference paper
Language
en
Open access
closed
Locations
Asilomar Conference on Signals, Systems and Computers (ACSSC) (publishedVersion)
Topics
  • Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
  • Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
  • Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
Keywords
sub-Nyquist sampling, compressed sensing, sparse signal recovery

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