Synthetic Aperture LADAR Automatic Target Recognizer Design and Performance Prediction via Geometric Properties of Targets (Preprint)

Abstract

Synthetic Aperture LADAR (SAL) has several phenomenology differences from Synthetic Aperture RADAR (SAR) making it a promising candidate for automatic target recognition (ATR) purposes. The diffuse nature of SAL results in more pixels on target. Optical wavelengths offers centimeter class resolution with an aperture baseline that is 10,000 times smaller than an SAR baseline. While diffuse scattering and optical wavelengths have several advantages, there are also a number of challenges. The diffuse nature of SAL leads to a more pronounced speckle effect than in the SAR case. Optical wavelengths are more susceptible to atmospheric noise, leading to distortions in formed imagery. While these advantages and disadvantages are studied and understood in theory, they have yet to be put into practice. This dissertation aims to quantify the impact switching from specular SAR to diffuse SAL has on algorithm design. In addition, a methodology for performance prediction and template generation is proposed given the geometric and physical properties of CAD models. This methodology does not rely on forming images, and alleviates the computational burden of generating multiple speckle fields and redundant ray-tracing.

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Document Details

Document Type
Technical Report
Publication Date
Feb 10, 2022
Accession Number
AD1159402

Entities

People

  • Jacob W. Ross

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Advanced Electronics
  • Human Systems
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Detectors
  • Electromagnetic Scattering
  • Image Recognition
  • Information Science
  • Machine Learning
  • Network Science
  • Neural Networks
  • Physical Properties
  • Ray Tracing
  • Scattering
  • Supervised Machine Learning
  • Synthetic Aperture Radar
  • Target Recognition

Fields of Study

  • Physics

Readers

  • Computer Vision.
  • Image Processing and Computer Vision.
  • Radar Systems Engineering.