A Suppression Method of Jamming against Radars Based on RF Features
Due to the development of digital radio frequency memory(DRFM),active jamming a-gainst radar mainlobes has become the mainstream in electronic warfare.Existing jamming sup-pression 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 convolu-tional 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 net-work(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 con-firmed.
jamming suppressionradio frequency(RF)featureconvolutional neural net worksig-nal reconstruction
NETL
NSTL
万方数据
