Modern radar systems often face various interference signals in complex and rapidly changing electronic environments. The task of suppressing this interference in the radar echo signal to extract vital information is challenging. A radar interference suppression method is proposed based on a generative adversarial network (GAN). This method effectively recovers the target signal from the echo signal, which contains interference and noise, by leveraging the powerful fitting ability of GAN. Specifically, this method was tested using coherent suppression interference, smart noise interference, and noise frequency modulation suppression interference. We compared the proposed GAN method with recurrent neural network, short-time Fourier transform time-varying filtering, short-time fractional Fourier transform time-varying filtering algorithms and RNN approach. The results show that the interference suppression algorithm based on GAN is superior to the other three algorithms.

中文翻译：

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基于生成对抗网络的雷达主动压制性干扰抑制

现代雷达系统经常面临复杂且快速变化的电子环境中的各种干扰信号。抑制雷达回波信号中的这种干扰以提取重要信息的任务具有挑战性。提出了一种基于生成对抗网络（GAN）的雷达干扰抑制方法。该方法利用GAN强大的拟合能力，有效地从含有干扰和噪声的回波信号中恢复出目标信号。具体地，使用相干抑制干扰、智能噪声干扰和噪声调频抑制干扰对该方法进行了测试。我们将所提出的 GAN 方法与循环神经网络、短时傅里叶变换时变滤波、短时分数傅里叶变换时变滤波算法和 RNN 方法进行了比较。结果表明，基于GAN的干扰抑制算法优于其他三种算法。