Radar emitter signal recognition based on feature selection and support vector machines

Authors:
Gexiang Zhang;Zhexin Cao;Yajun Gu;Weidong Jin;Laizhao Hu
Affiliations:
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China;College of Profession and Technology, Jinhua, Zhejiang, China;School of Computer Science, Southwest University of Science and Technology, Mianyang, Sichuan, China;School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China;National EW Laboratory, Chengdu, Sichuan, China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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Abstract

One of the intelligent aspects of human beings in pattern recognition is that man identifies an object in real world using Marked Characteristic Principle (MCP). This paper proposes a humanoid recognition method for radar emitter signals. The main points of the method include feature ordering and an improved one-versus-rest multiclass classification support vector machines. According to MCP, an approach for computing marked characteristic coefficients is presented to obtain the most marked feature of every radar emitter signal. Subsequently, a support vector network is designed using the improved one-versus-rest combination approach of several binary support vector machines. Experimental results show that the introduced method has faster recognition speed and better classification capability than conventional recognition approaches.