Neural Networks for Sequential Discrimination of Radar Targets

Abstract

In this paper, perceptron neural networks are applied to the problem of discriminating between two classes of radar returns. The perceptron neural networks are used as nonlinearities in two threshold sequential discriminators which act upon samples of the radar return. The test statistic compared to the thresholds is of the form T sub n(Z) = sum over j=1 to n-K+1 of [gamma(Zj,Zj+1,... ,Zj+K-1)] where Z sub i, i = 1,2,3,... are the radar samples and gamma() is the nonlinearity formed by the neural network. Numerical results are presented and compared to existing discrimination schemes.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1991
Accession Number
ADA454861

Entities

People

  • Evaggelos A. Geraniotis
  • Joseph A. Haimerl

Organizations

  • University of Maryland

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Discrimination
  • Information Operations
  • Neural Networks
  • Radar Targets
  • Targets
  • Universities

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

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