Compressive Sensing Matrix Design and Evaluation with Cognitive Radio Applications

Abstract

This dissertation is aimed at evaluating a sensing matrix in an overcomplete compressive sensing (CS) system. The main theoretical problems addressed are: Given a redundant (overcomplete)dictionary, a sensing matrix, and a greedy approximation method such as orthogonal matching pursuit, what tools can ascertain the suitability of a given sensing matrix, in the course of performing CS with the dictionary? In general, how does the relationship between the sensing matrix and dictionary (regarding structure/composition) affect the greedy support estimation of a sparse signal?

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2018
Accession Number
AD1086330

Entities

People

  • John J. Kelly

Organizations

  • Rome Laboratory

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Cognitive Radio
  • Compressed Sensing
  • Computational Science
  • Databases
  • Detection
  • Detectors
  • Distribution Functions
  • Electrical Engineering
  • Information Science
  • Linear Algebra
  • Linear Programming
  • Mathematical Models
  • Network Science
  • Normal Distribution
  • Signal Processing
  • Test And Evaluation

Fields of Study

  • Engineering

Readers

  • Linear Algebra
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.