舰船电子对抗

由于数字射频存储器(DRFM)的发展，针对雷达主瓣的有源干扰已成为电子战的主流。现有的干扰抑制方法较多针对单一的干扰，泛化性较弱。提出先用基于卷积神经网络(CNN)的堆栈式卷积自编码器(SCAE)提取雷达信号的射频特征，再将射频特征应用于基于深度神经网络(DNN)的堆栈式自编码器(SAE),从而实现干扰抑制。最后，通过实际采集的数据验证了所提方法的有效性，并确定了理论分析的准确性。

Due to the development of digital radio frequency memory(DRFM),active jamming against radar mainlobes has become the mainstream in electronic warfare.Existing jamming suppression methods often focus on individual jamming, and the generalization is weak.In this paper, a novel approach is proposed.Firstly, a stacked convolutional autoencoder(SCAE) based on convolutional neural network(CNN) is utilized to extract the radio frequency features of radar signals.Subsequently, these features are applied to a stacked autoencoder(SAE) based on deep neural network(DNN),thereby jamming suppression is realized.Finally, the effectiveness of the proposed method is validated through actual collected data, and the accuracy of theoretical analysis is confirmed.