In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.

中文翻译：

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通过IIF-Net深度学习模型的雷达信号识别方法。

在现代战场日益复杂的电磁环境中，如何快速，准确地识别雷达信号是电子对策领域的一个热点。本文使用USRP N210，USRP-LW N210和其他通用软件无线电外围设备来模拟雷达信号的发送和接收过程，总共有8个雷达信号，即Barker，Frank，混沌，P1，P2产生P3，P4和OFDM。该信号通过Choi–Williams分布函数（CWD）获得时频图像（TFI）。根据雷达信号TFI的特点，设计了全局特征平衡提取模块（GFBE）。然后，提出了一种新的具有更少网络参数和更少计算成本的IIF-Net卷积神经网络。在实验中，信噪比（SNR）范围为-10至6 dB。实验表明，当SNR高于-2 dB时，IIF-Net的信号识别率高达99.74％，而当SNR为-10 dB时，信号识别精度仍为92.36％。与其他方法相比，IIF-Net在低信噪比下具有更高的识别率和更好的鲁棒性。