Application of Neutral Networks in the Estimation of Two-Dimensional Target Orientation

Abstract

A new method for the robust estimation of target orientation using measured radar cross section is proposed. The method is based on a Generalized Regression Neural Network (GRNN) scheme. The network is trained by the FFT modulus of bistatic radar cross section data sampled at the receiver positions. The target value to be trained is the angle between a defined target orientation and the incident wave. Results based on actual measurements are presented.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2003
Accession Number
ADP014211

Entities

People

  • A. Kabiri
  • K. Barkeshli
  • N. Sarshar

Organizations

  • University of Tehran

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Data Sets
  • Electrical Engineering
  • Engineering
  • Frequency
  • Geometry
  • Integral Equations
  • Neural Networks
  • Plane Waves
  • Probability
  • Probability Density Functions
  • Random Variables
  • Scattering
  • Target Classification
  • Target Recognition
  • Two Dimensional

Readers

  • Neural Network Machine Learning.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks