Pattern Recognition Techniques for Radar Aircraft Identification.

Abstract

Some problems concerning the design and evaluation of a pattern recognition system for low-frequency radar aircraft identification are investigated. A decision-theoretic model that includes a model of noise perturbations of the feature-vector, and a model of uncertainity associated with the measurement of parameter values has been developed. The performance of the minimum-risk classifier based on the decision-theoretic model is compared to those of the nearest-neighbor classifiers. This comparison is made in terms of the following measures: misclassification probability, classification bias, computational efficiency and robustness. A feature-extraction algorithm based on the decision-theoretic model is developed. Alternative procedures for the problem where multiple radar scans are available have been compared. These procedures include a sequential procedure based on the decision-theoretic model, and a procedure based on taking a majority-vote over multiple decisions of a given classifier. (Author)

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1975
Accession Number
ADA020754

Entities

People

  • L. J. White
  • S. N. Srihari

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Classification
  • Efficiency
  • Extraction
  • Feature Extraction
  • Frequency
  • Identification
  • Machine Learning
  • Mathematics
  • Measurement
  • Pattern Recognition
  • Perturbations
  • Probability
  • Recognition

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms