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This thesis investigates different approaches to enable the use of compressed sensing (CS)-based acquisition devices in resource-constrained environments relying on cheap, energy-efficient sensors. We consider the acquisition of structured low-complexity signals from excessively quantized 1-bit observations, as well as partial compressive measurements collected by one or multiple sensors. In both scenarios, the central goal is to alleviate the complexity of sensing devices in order to enable signal acquisition by simple, inexpensive sensors.

In the first part of the thesis, we address the reconstruction of signals with a sparse Fourier transform from 1-bit time domain measurements. We propose a modification of the binary iterative hard thresholding algorithm, which accounts for the conjugate symmetric structure of the underlying signal space. In this context, a modification of the hard thresholding operator is developed, whose use extends to various other (quantized) CS recove

Quantized Compressive Sampling for Structured

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    A Paperback by Niklas Koep

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      Book details

      Publisher Verlag G. Mainz
      Published 7 December 2019
      ISBN-13 9783958862913
      978-3958862913
      ISBN-10 3958862918
      Also in:
      Electrical engineering

      Description

      Book Synopsis

      This thesis investigates different approaches to enable the use of compressed sensing (CS)-based acquisition devices in resource-constrained environments relying on cheap, energy-efficient sensors. We consider the acquisition of structured low-complexity signals from excessively quantized 1-bit observations, as well as partial compressive measurements collected by one or multiple sensors. In both scenarios, the central goal is to alleviate the complexity of sensing devices in order to enable signal acquisition by simple, inexpensive sensors.

      In the first part of the thesis, we address the reconstruction of signals with a sparse Fourier transform from 1-bit time domain measurements. We propose a modification of the binary iterative hard thresholding algorithm, which accounts for the conjugate symmetric structure of the underlying signal space. In this context, a modification of the hard thresholding operator is developed, whose use extends to various other (quantized) CS recove

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