DRFM Interference Recognition Based on Deep Learning

For digital radio frequency memory (DRFM) to generate signals cannot be effectively distinguished from the source signal, 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 signal 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 recognition accuracy is good.