Identification of Catalogued and Uncatalogued Classes.

Abstract

A technique to discriminate listed objects from unlisted ones is described. It is based on the principle of minimizing the probability of error in an identification process. The devised classifier is implemented as a threshold test. The classifier was applied to an aircraft identification problem. It was shown that the error of misclassifying catalogued targets as uncatalogued and vice versa can be made very small, while keeping a high probability of correct identification when the presence of a listed object is detected. The implementation of the developed scheme was shown to be simple and efficient. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1978
Accession Number
ADA064843

Entities

People

  • Heng-cheng Lin

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Aircrafts
  • Airplanes
  • Algorithms
  • Artificial Intelligence
  • Computations
  • Distribution Functions
  • Gaussian Noise
  • Government Procurement
  • Monte Carlo Method
  • New York
  • Probability
  • Probability Density Functions
  • Random Variables
  • Simulations
  • Statistics
  • Three Dimensional
  • Two Dimensional

Readers

  • Geodesy
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.