基于深度学习的DRFM信号识别
DRFM Interference Recognition Based on Deep Learning
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摘要: 针对数字射频存储器(DRFM)产生信号与源信号之间无法有效区分的问题,运用基于小波变换的同步压缩变换将时域的雷达信号转换为时频图,运用深度学习强大的图像识别能力,实现了基于深度学习的源信号与DRFM 信号识别,从而解决了在雷达信号处理中无法有效区分回波信号和DRFM 欺骗信号以及在雷达干扰识别中基于DRFM 的欺骗干扰难以识别的问题。为了验证深度学习过程的可靠性,通过神经网络可解释性算法对训练结果进行了验证和分析。实验结果表明,相比于识别原始信号,识别DRFM 信号神经网络需要用到更多的特征,神经网络判断准确率达到了96. 33??,识别精度良好。Abstract: 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.
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