Statistical pattern recognition techniques as applied to radar returns

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.