New Algorithms and Sparse Regularization for Synthetic Aperture Radar Imaging

Abstract

The PI led a collaborative effort to quantify the super-resolution potential of different computational methods for the directionfinding problem in sensing and surveillance. The difficulty of super-resolution is summarized in three quantities (the super-resolution factor, the signal-to-noise ratio, and the number of targets), and tight scalings between these quantities are presented to decide whether some methods can succeed -- or every method must fail -- at the target detection task. The analysis identifies the algorithms that perform well, and those that don't, even in the case of targets that shadow each other (nearby azimuths, different ranges).

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Document Details

Document Type
Technical Report
Publication Date
Oct 26, 2015
Accession Number
ADA625751

Entities

People

  • Laurent Demanet

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Antenna Arrays
  • Arrays
  • Computational Science
  • Detection
  • Direction Finding
  • Electronic Mail
  • Fourier Analysis
  • Measurement
  • Phase Transformations
  • Radar Imaging
  • Signal Processing
  • Statistical Analysis
  • Synthetic Aperture Radar
  • Target Detection

Readers

  • Computer Vision.
  • Radar Systems Engineering.
  • Systems Analysis and Design