Syntactic Pattern Recognition for Radar Target Identification.

Abstract

A syntactic pattern recognition system is proposed for use in radar target identification. The system utilizes one of three different level-crossing based pattern representation schemes. Likelihood functions are estimated from relative frequency densities and are used for classification of the symbolic pattern representation. Grammatical inference is used to derive a syntax analysis algorithm from the likelihood function classifier performance is estimated using Monte-Carlo simulations, Directions of further research on this promising topic are discussed.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1987
Accession Number
ADA182330

Entities

People

  • F. D. Garber
  • O. S. Sands

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Azimuth
  • Electrical Engineering
  • Identification
  • Identification Systems
  • Language
  • Low Noise
  • Measurement
  • Monte Carlo Method
  • Noise
  • Pattern Recognition
  • Probability
  • Radar
  • Radar Targets
  • Random Variables
  • Simulations

Readers

  • Computational Linguistics
  • Molecular Photonics/Laser Physics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Translation