Radar Emitter Signal Recognition Based on EMD and Neural Network

Abstract

Radar emitter signal (RES) recognition is the important content in radar reconnaissance and signal processing. In order to study the problem of RES recognition, and to improve the RES recognition rate of the electronic warfare equipment, the empirical mode decomposition (EMD) theory and wavelet packet (WP) are introduced into RES feature extraction. A new RES recognition method is proposed based on WP, EMD and neural network (NN). It uses wavelet packet to finish decomposition, de-noising and reconstruction of the RES. Then obtain the intrinsic mode function (IMF) through EMD, which can embody the characteristics of the RES. The energy of each IMF are calculated and normalized, which would be regarded as the feature vector. By constructing back propagation neural network (BPNN) classifier and redial basis function neural network (RBFNN) classifier, it realizes the RES recognition finally. Experiment results show that the RES recognition method based on WP, EMD and NN is an effective recognition method, which can achieve satisfying correct recognition rate in a larger signal to noise ratio, and has certain reference value in follow-up in-depth study.