Feature Selection Applied to Radar Target Identification.

Abstract

Three feature selection algorithms are investigated and applied to characterize optimum sets of frequencies for radar target identification. One algorithm is of the nonparametric discriminant analysis type, the other two algorithms, the pairwise exponential weight distance algorithm and the pairwise probability of error algorithm, are parametric and incorporate information about the measurement noise into the feature selection process. The utility of these feature selection algorithms for radar target identification is then evaluated through Monte-Carlo simulations. It is found that significant gain in classification performance can be achieved by using the optimum sets of frequencies characterized by the parametric algorithms.

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1987
Accession Number
ADA183880

Entities

People

  • F. D. Garber
  • Ogmundur Snorrason

Organizations

  • Ohio State University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Data Science
  • Discriminant Analysis
  • Feature Selection
  • Frequency
  • Identification
  • Information Science
  • Monte Carlo Method
  • Radar Targets
  • Targets

Readers

  • Radar Systems Engineering.
  • Regression Analysis.