Sigma Delta quantization for compressive sensing

Petros Boufounos, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

Compressive sensing is a new data acquisition technique that aims to measure sparse and compressible signals at close to their intrinsic information rate rather than their Nyquist rate. Recent results in compressive sensing show that a sparse or compressible signal can be reconstructed from very few measurements with an incoherent, and even randomly generated, dictionary. To date the hardware implementation of compressive sensing analog-to-digital systems has not been straightforward.. This paper explores the use of Sigma-Delta quantizer architecture to implement such a system. After examining the challenges of using Sigma-Delta with a randomly generated compressive sensing dictionary, we present efficient algorithms to compute the coefficients of the feedback loop. The experimental results demonstrate that Sigma-Delta relaxes the required analog filter order and quantizer precision. We further demonstrate that restrictions on the feedback coefficient values and stability constraints impose a small penalty on the performance of the Sigma-Delta loop, while they make hardware implementations significantly simpler.

Original languageEnglish (US)
Title of host publicationWavelets XII
Volume6701
DOIs
StatePublished - Dec 1 2007
EventWavelets XII - San Diego, CA, United States
Duration: Aug 26 2007Aug 29 2007

Other

OtherWavelets XII
CountryUnited States
CitySan Diego, CA
Period8/26/078/29/07

Keywords

  • Compressive sensing
  • Quantization
  • Sigma Delta

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

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