Feature Extraction of Radar Emitter Signal Based on Empirical Mode Decomposition

Abstract

To enhance accurate recognition rate of radar emitter signal (RES), a novel feature extraction method of radar emitter signal is proposed based on empirical mode decomposition (EMD) theory. The EMD algorithm is used to decompose the radar emitter signal into a number of intrinsic mode functions (IMF) and a residue component, these IMFs can reflect characteristics of the radar emitter signal. After that, the energy of each IMF is calculated and normalized, which is regarded as an element of the feature vector. Finally, it realizes the recognition of radar emitter signal through BP neural networks (BPNN). Experiment results shows that EMD-based feature extraction method of radar emitter signal is an effective method, energy feature that extraction from EMD decomposition has a higher recognition rate. The main work of this paper is that it applied the EMD method to feature extraction of radar emitter signal for the first time.