Distributed RF Systems for Close-In Sensing and Imaging

Abstract

The goal of this research is to improve close-in sensing and imaging applications through a new formulation of RF tomography, which is considered because of its flexibility and advantages when dealing with distributed sensors. This research is motivated by the removal of some limitations in the current formulation of RF tomography. One limitation is the first order Born approximation that is physically equivalent to neglecting multiple scattering phenomena. This limitation is overcome with the quadratic forward model. A second limitation is associated with the contrast function, which represents the unknown quantity to be reconstructed. In fact, while the initial formulations of RF Tomography have been based upon a scalar contrast function, it is possible to extract more information by using a dyadic contrast function, which takes advantage of the vector nature of both the incident and scattered electromagnetic field. A third limitation is the current existing inversion algorithms that do not allow for the introduction of prior knowledge to compute the solution of imaging problems, which is overcome with the introduction of physical bounds into iterative solution methods.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2016
Accession Number
AD1006928

Entities

People

  • Danilo Erricolo

Organizations

  • University of Illinois at Chicago

Tags

Communities of Interest

  • Advanced Electronics
  • Biomedical
  • Sensors

DTIC Thesaurus Topics

  • Applied Mathematics
  • Bessel Functions
  • Cartesian Coordinates
  • Communication Systems
  • Computational Fluid Dynamics
  • Computational Science
  • Computers
  • Coordinate Systems
  • Detection
  • Detectors
  • Dielectric Permittivity
  • Dielectrics
  • Differential Equations
  • Diffraction
  • Electromagnetic Fields
  • Electromagnetic Scattering
  • Electromagnetism
  • Far Field
  • Fluoropolymers
  • Geometry
  • Inverse Problems
  • Magnetic Fields
  • Radar
  • Random Variables
  • Scattering
  • Signal Processing

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.
  • Plasma Physics / Magnetohydrodynamics