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

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.