In low signal-to-noise ratio (SNR) environments, the traditional radar emitter recognition (RER) method struggles to recognize multiple radar emitter signals in parallel. This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network. This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs. First, we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform (STFT). The time-frequency distribution image is then denoised using a deep normalized convolutional neural network (DNCNN). Secondly, the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established, and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model. Finally, time-frequency image is recognized and classified through the model, thus completing the automatic classification and recognition of the time-domain aliasing signal. Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.

在低信噪比 (SNR) 环境中，传统的雷达发射器识别 (RER) 方法难以并行识别多个雷达发射器信号。本文提出了一种基于残差网络的多种雷达-发射机调制类型的多标签分类识别方法。该方法可以在低信噪比下快速对多调制雷达时域混叠信号进行并行分类识别。首先，我们对接收到的信号进行时频分析，通过短时傅里叶变换（STFT）提取归一化的时频图像。然后使用深度归一化卷积神经网络 (DNCNN) 对时频分布图像进行去噪。其次，建立多调制雷达发射机时域混叠信号的多标签分类识别模型，学习雷达信号时频分布图像数据集的特征，达到训练模型的目的。最后通过模型对时频图像进行识别分类，从而完成时域混叠信号的自动分类识别。仿真结果表明，该方法可以在低信噪比下并行分类识别不同调制类型的雷达发射器信号。从而完成时域混叠信号的自动分类识别。仿真结果表明，该方法可以在低信噪比下并行分类识别不同调制类型的雷达发射器信号。从而完成时域混叠信号的自动分类识别。仿真结果表明，该方法可以在低信噪比下并行分类识别不同调制类型的雷达发射器信号。