
Accession Number : AD0717198
Title : The Linear Separability of MultipleFrequency Radar Returns, With Applications to Target Classification.
Descriptive Note : Technical rept.,
Corporate Author : OHIO STATE UNIV RESEARCH FOUNDATION COLUMBUS
Personal Author(s) : Repjar,Andrew G.
Report Date : 16 NOV 1970
Pagination or Media Count : 106
Abstract : Methods are presented for distinguishing and identifying object classes from a limited number of radar measurements. The radar measurements are the backscattered signal amplitudes at up to twelve harmonically related frequencies in the lower portion of the objects spectrum. The amplitudes of n radar returns of an object are defined as an ndimensional vector. A class of vectors is defined for each object by a representative set of vectors which cover the various aspects of the objects. The classes of vectors corresponding to different objects are linearly mapped into a betaspace of reduced dimension. The separability of these points in betaspace is demonstrated, indicating a correct object classification. The maximum and average probabilities of misclassification are derived for classes immersed in gaussian noise. Mapping methods are derived which minimize classification errors. The probability of misclassification is shown to increase, but nonuniformly as the dimensionality of the nspace is reduced, indicating that the returns at higher frequencies do not contribute as significantly to the reduction of the probability of error as the lower frequencies. Finally it is shown that the techniques used for improving the noise resistance of pairwise classifiers apply also to multiclass classifiers. (Author)
Descriptors : (*RADAR TARGETS, TARGET RECOGNITION), RADAR REFLECTIONS, CLASSIFICATION, PROBABILITY
Subject Categories : Active & Passive Radar Detection & Equipment
Distribution Statement : APPROVED FOR PUBLIC RELEASE