DRFM Interference Recognition Based on Deep Learning
For digital radio frequency memory(DRFM)to generate signals cannot be effectively distinguished from the source sig-nal,using synchro squeeze wavelet transform the radar signal of the time domain is converted to the time frequency diagram.Using deep learning powerful image recognition capabilities,the source signal and DRFM signal recognition based on deep learning are implemented.The problem that the echo signal cannot be effectively distinguished from the DRFM deception signal in the radar sig-nal processing is resolved.The problem that is difficult to recognize DRFM deceptive interference in radar interference recognition is resolved also.In order to verify the reliability of the deep learning process,the training results are verified and analyzed through the explanatory algorithm of neural networks.The accuracy of the neural network judgment has reached 96.33%,and the recogni-tion accuracy is good.
interference identificationtime-frequency conversiongradient-weighted class activation mapping(Grad-CAM)guided-back propagationdeep learning
万方数据
维普
