Accession Number : ADA111893

Title :   Statistical Pattern Recognition Techniques as Applied to Radar Returns.

Descriptive Note : Final technical rept. Apr-Sep 79,

Corporate Author : MICHIGAN TECHNOLOGICAL UNIV HOUGHTON

Personal Author(s) : Fordon,W A ; Fraser,A A

PDF Url : ADA111893

Report Date : Dec 1981

Pagination or Media Count : 179

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)

Descriptors :   *Radar reflections, *Pattern recognition, *Ground clutter, *Radar signals, Statistical processes, Backscattering, Statistical decision theory, Simulation, Parametric analysis, Interfaces, Data bases, Rates, Algorithms, Hypotheses, Classification, Radar, Limitations, Errors, Bayes theorem

Subject Categories : Active & Passive Radar Detection & Equipment
      Radiofrequency Wave Propagation

Distribution Statement : APPROVED FOR PUBLIC RELEASE