Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals with different signal-to-noise ratio (SNR) levels from −14 to 20 dB. First, raw signal sequences in the time domain, frequency domain and autocorrelation domain are as input for a shallow CNN. Then the features extracted by CNN will be the input of LSTM network. Finally, DNNs will output the signal modulation types directly. The simulation results demonstrate that the accuracies in autocorrelation domain are all more than 90% at −6 dB and close to 100% when SNR > −2 dB. The recognition performances of the three domains are compared. Compared with other recognition methods, the proposed method has higher average accuracy and better performance under low SNR condition. The measured results show that the proposed method has achieved high accuracies of common four kinds of measured radar signals.

中文翻译：

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基于CLDN网络的脉内调制雷达信号识别

本研究通过深度学习网络研究了雷达信号的自动调制分类，该分类在民用和军事应用中均起着重要作用。在这项研究中，提出了一种结合浅层卷积神经网络（CNN），长短期记忆（LSTM）网络和深层神经网络（DNN）的新型网络，以识别六种类型的具有不同信噪比的雷达信号（SNR）等级从-14到20 dB。首先，将时域，频域和自相关域中的原始信号序列作为浅层CNN的输入。然后，CNN提取的特征将成为LSTM网络的输入。最后，DNN将直接输出信号调制类型。仿真结果表明，自相关域的精度在-6 dB时均超过90％，而在SNR> -2 dB时接近100％。比较了三个域的识别性能。与其他识别方法相比，该方法在低信噪比条件下具有更高的平均准确度和更好的性能。实测结果表明，该方法对常见的四种实测雷达信号均具有较高的精度。