Theory and Practice of Compressed Sensing in Communications and Airborne Networking

Abstract

We consider the problem of compressed sensing and propose new deterministic constructions of compressive sampling matrices based on finite-geometry generalized polygons. For the noiseless measurements case, we develop a novel recovery algorithm for strictly sparse signals that utilizes the geometry properties of generalized polygons and exhibits complexity linear in the sparsity value. In the presence of measurement noise, recovery of the generalized-polygon sampled signals can be carried out most effectively using a belief propagation algorithm. Experimental studies included in this report illustrate our theoretical developments.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2010
Accession Number
ADA535407

Entities

People

  • Stella N. Batalama

Organizations

  • University at Buffalo

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Compressed Sensing
  • Compression Ratio
  • Construction
  • Geometry
  • Government Procurement
  • Governments
  • Information Exchange
  • Linear Programming
  • Measurement
  • New York
  • Probability
  • Random Variables
  • Recovery
  • Sampling

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Graph Algorithms and Convex Optimization.
  • Mechanical Engineering/Mechanics of Materials.

Technology Areas

  • Space