A Dispersive Scattering Center, Parametric Model for 1-D ATR

Abstract

The dispersive scattering center (DSC) model characterizes high-frequency backscatter from radar targets as a finite sum of localized scattering geometries distributed in range, these geometries, along with their relative locations, can be conveniently used as features in a one-dimensional automatic target recognition (ATR) algorithm. The DSC model's type and range parameters correspond to geometry and distance features according to the geometric theory of diffraction (GTD). Since these parameters are estimated in the phase history domain of the radar signal, the range parameter does provide superresolution in the time domain. To demonstrate the viability of feature extraction based on the DSC model's range and type parameters, a four class ATR experiment was performed. The experimental data contains 301 direct range measurements each for four model aircraft of similar size and shape at 0 degrees elevation and from 0 to 30 degrees azimuth. After implementing DSC model feature extraction on this data, a fully-connected, two-layer neural net obtained over 98% classification accuracy. In addition, DSC model feature extraction offers an approximate 85% reduction in the number of features compared to the numerous Fourier bin magnitudes in template matching approaches to ATR.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1997
Accession Number
ADA335655

Entities

People

  • Dane F. Fuller

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Diffraction
  • Electrical Engineering
  • Electromagnetic Scattering
  • Engineering
  • Feature Extraction
  • Frequency Response
  • Geometry
  • Image Processing
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Signal Processing
  • Synthetic Aperture Radar
  • Target Recognition
  • Two Dimensional

Readers

  • Electromagnetic Wave Scattering and Antenna Radiation Engineering
  • Neural Network Machine Learning.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference