Statistical Pattern Recognition Techniques as Applied to Radar Returns.

Abstract

This report presents a summary of the basic principles of pattern recognition and statistical decision theory and applies them to the problem of classifying radar returns. While pattern recognition techniques have been applied to radar signal detection problems, they have rarely been used in testing hypothesis for classifying radar returns. Two techniques, the parametric Bayes and the non-parametric K-Nearest Neighbor algorithms, were compared using simulated radar backscatter data. The error rate of these algorithms was the chief criterion used for the evaluation of performance. The results showed that the Nearest Neighbor technique gives a smaller error rate than the Bayes technique for the limited data sets tested. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1981
Accession Number
ADA111893

Entities

People

  • A. A. Fraser
  • W. A. Fordon

Organizations

  • Michigan Technological University

Tags

Communities of Interest

  • Biomedical
  • Human Systems
  • Sensors

DTIC Thesaurus Topics

  • Composite Materials
  • Computer Programs
  • Computers
  • Data Analysis
  • Data Mining
  • Data Science
  • Databases
  • Delta Functions
  • Detectors
  • Frequency
  • Information Processing
  • Information Science
  • Metal Matrix Composites
  • Network Science
  • Pattern Recognition
  • Statistical Analysis
  • Statistical Decision Theory

Readers

  • Neural Network Machine Learning.
  • Radar Systems Engineering.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference