Radar emitter signal recognition based on support vector machines

Abstract

Radar emitter signal recognition plays an important role in electronic intelligence systems and electronic support measure systems. To heighten accurate recognition rate of radar emitter signals, this paper proposes a hierarchical classifier structure to recognize radar emitter signals. The proposed structure combines resemblance coefficient classifier, support vector machines with binary tree architecture and linear classifier based on Mahalanobis distance. Experimental results of recognizing multiple radar emitter signals show that the introduced classifier is simpler, consumes smaller training time and achieves higher accurate recognition rate and greater efficiency, in comparison with one-versus-rest support vector machines, one-versus-one support vector machines and binary-tree support vector machines.