Paper
27 September 2007 Sigma delta quantization for compressive sensing
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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.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Petros Boufounos and Richard G. Baraniuk "Sigma delta quantization for compressive sensing", Proc. SPIE 6701, Wavelets XII, 670104 (27 September 2007); https://doi.org/10.1117/12.734880
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Cited by 21 scholarly publications and 1 patent.
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KEYWORDS
Associative arrays

Compressed sensing

Quantization

Feedback loops

Analog electronics

Computing systems

Linear filtering

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