Radar Emitter Identification Based on Deep Convolutional Neural Network
Mingxin Kong, Jing Zhang, Weifeng Liu, Guilin Zhang · 2018
Aiming at the identification and classification of radar radiation sources, this paper proposes a classification method based on the Convolutional Neural Network(CNN) for radar signal classification. Firstly, this paper sets the appropriate learning rate, batch size, iteration number, momentum and weight decay coefficient. Secondly, the time domain real part waveform signal is modeled and the network structure is selected for analysis. Finally, according to the spectrogram of the time domain waveform by Short Time Fourier Transform(STFT), design two different convolutional neural network models. The results show that the network learns more distinguishing feature representations and has better generalization capabilities after STFT. The Deep Learning of CNN has a greater advantage in extracting the feature representations in the spectrogram of the radar signal.