A Compressed Sensing Approach to Signal Fragmentation

Abstract

This project is on a mathematical approach to signal fragmentation, as proposed by Dr. Richard Albanese, including mathematical formulation, analysis and optimization. The proposal of Albanese is for a method to send (relatively) long wavelength signals over an array of small antennas. Signal fragmentation approximates a desired signal f(t) (the input current to the antenna) by a sum of wavelets, each of which has the same shape phi(t), but with the n-th term shifted in time by an amount tn and scaled by a factor an. The wavelet phi is assumed to have compact support in time and successive wavelets are sent over different antennas, so that none of the fragments overlap in time. The objective of our project was to improve the design process, the accuracy, and the efficiency of signal fragmentation. Although we originally proposed to apply methods from compressed sensing and related fields, we found a better approach based on harmonic analysis, wavelets and approximation theory. With this approach, we succeeded in answering most of the questions that emerged from the research of Albanese, including development of a unified theory from antenna input to far field, analysis of spectral leakage for signal fragmentation, elimination of spectral leakage for sinusoidal signals, approximation of AM signals, energy efficiency of signal fragmentation, and optimal choice of wavelet.

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Document Details

Document Type
Technical Report
Publication Date
Jun 28, 2019
Accession Number
AD1086110

Entities

People

  • Russel E. Caflisch
  • Stanley Osher

Organizations

  • University of California

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Compressed Sensing
  • Department Of Defense
  • Efficiency
  • Elimination
  • Energy Efficiency
  • Far Field
  • Fourier Analysis
  • Fragmentation
  • Harmonic Analysis
  • Long Wavelengths
  • Optimization
  • Scientific Research
  • Standards

Fields of Study

  • Engineering

Readers

  • Electromagnetic Wave Scattering and Antenna Radiation Engineering
  • Explosive Engineering.
  • Neural Network Machine Learning.