Emitter identification based on multi-class spectrograms in time-frequency domain

Abstract

In the identification of emitter, there are some problems such as poor identification effect and incomplete feature expression of emitter by using single signal processing method to obtain spectrogram with feature of emitter. In order to solve these problems, a method of emitter identification based on multi-class spectrograms in time-frequency domain is proposed. Specifically, three time-frequency analyses, including short-time Fourier transform (STFT), smoothed pseudo Wigner-Ville distribution (SPWVD) and reduced interference distribution with Hanning kernel (RIDHK), are performed on signal data to obtain the time-frequency spectrograms with different feature expressions. Three convolutional neural networks (CNN) were used to extract the independent features from three classes of spectrograms, and the three features were stacked to obtain more complete features of emitter, to improve the recognition effect. Experimental results show that compared with the single feature recognition method, the proposed method improves the accuracy of emitter identification effectively.