Sparse Aperture Multistatic Radar Imaging Techniques: Final Report

Abstract

We report outcomes for an NRL 6.1 Base Program project towards the development of new multistatic-multiview radar imaging techniquesfrom sparse sensors. The algorithmic framework for the techniques is the Linear Sampling Method (LSM), which does not require linear scattering assumptions common to most radar techniques and thus may create imagery of fundamentally higher fidelity. The new techniques are formulated in order to overcome the main challenge to practical imaging with the LSM its need for impractically dense and wide-angle sensor placement. We present several new techniques and demonstrate significantly improved image fidelity from simulated and experimental target data.

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Document Details

Document Type
Technical Report
Publication Date
Jan 03, 2022
Accession Number
AD1156123

Entities

People

  • Hatim F. Alqadah
  • Matthew J. Burfeindt

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Compressed Sensing
  • Detectors
  • Dielectric Permittivity
  • Dielectric Properties
  • Electrical Properties
  • Geometry
  • Imaging Techniques
  • Machine Learning
  • Neural Networks
  • Resonant Frequency
  • Supervised Machine Learning
  • Synthetic Aperture Radar
  • Target Recognition
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Neural Network Machine Learning.
  • Phased Array Antenna Design.
  • Sensor Fusion and Tracking Systems.