Accession Number : ADA183880
Title : Feature Selection Applied to Radar Target Identification.
Descriptive Note : Technical rept.,
Corporate Author : OHIO STATE UNIV COLUMBUS ELECTROSCIENCE LAB
Personal Author(s) : Snorrason,Ogmundur ; Garber,F. D.
Report Date : JUL 1987
Pagination or Media Count : 176
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.
Descriptors : *CLASSIFICATION, *MONTE CARLO METHOD, *SIMULATION, *PARAMETRIC ANALYSIS, *RADAR TARGETS, *TARGET RECOGNITION, *DISCRIMINATE ANALYSIS, *NONPARAMETRIC STATISTICS, *ALGORITHMS, SELECTION, MEASUREMENT, NOISE, FREQUENCY, OPTIMIZATION, ERRORS, PROBABILITY
Subject Categories : Active & Passive Radar Detection & Equipment
Distribution Statement : APPROVED FOR PUBLIC RELEASE