To deal with problems of uncertain modulations and multiple pulse widths in pulse waveforms (PWs) during the identifying procedure, a novel specific emitter identification (SEI) method based on PW images (PWIs) and convolutional neural network is proposed. In the method, a more accurate signal model is built with considering the rising, steady and falling part of the whole PW based on actual radar pulse signals. PWI achieves transforming time-domain waveforms to 2D binary images as an SEI analysis feature. To match the PWI feature, a convolutional neural network with the small convolutional kernel is designed to extract the subtle features and finish the supervised training. By tuning the parameters of the convolutional neural network, it completes a balance of consuming time and identifying accuracy. Simulations and experiments indicate that the proposed method outperforms the existed methods on identifying radar individuals with uncertain modulations and multiple pulse widths in the intercepted pulse signals.

中文翻译：

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卷积神经网络在基于脉冲波形图像的特定辐射源识别中的应用

针对识别过程中脉冲波形（PW）中调制不确定，脉冲宽度多的问题，提出了一种基于PW图像（PWI）和卷积神经网络的新型比发射器识别（SEI）方法。在该方法中，基于实际雷达脉冲信号，考虑了整个PW的上升，稳定和下降部分，建立了更准确的信号模型。作为SEI分析功能，PWI实现了将时域波形转换为2D二进制图像。为了匹配PWI特征，设计了带有小卷积核的卷积神经网络，以提取细微特征并完成监督训练。通过调整卷积神经网络的参数，可以完成耗时和识别准确性之间的平衡。