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Individual Identification Technology of Communication Radiation Sources Based on Deep Learning

机译：基于深度学习的通信辐射源的个​​人识别技术

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With the rapid development of wireless communication, the types of radiation sources are numerous and complex, and the signals are diversified. Traditional methods of identifying individual radiation sources may no longer meet the needs of society. The identification of individual wireless radiation sources is of great significance for ensuring the security of communication systems and improving the ability of military communication reconnaissance and countermeasures, but most of them use traditional identification methods. This article introduces deep learning as a classification method. Since there is no suitable public data set, we use 6 USRP devices of the same model, combined with LabVIEW software, in a laboratory environment to collect the IQ signals emitted by 5 individual radiation sources, perform preprocessing, and then invest in neural network training. The collected data can be used as a public data set for individual identification in the future. Using a variety of neural network structures and adjusting the parameters, we have obtained a more satisfactory classification effect.
机译：随着无线通信的快速发展，辐射源的类型无数且复杂，并且信号是多样化的。识别单个辐射源的传统方法可能不再满足社会的需求。个人无线辐射源的识别对于确保通信系统的安全性以及提高军事通信侦察能力和对策的能力具有重要意义，但其中大多数都使用传统的识别方法。本文介绍了深度学习作为分类方法。由于没有合适的公共数据集，我们使用相同型号的6个USRP设备，与LabVIEW软件相结合，在实验室环境中收集5个单独的辐射源发出的IQ信号，执行预处理，然后投资神经网络培训。收集的数据可以用作未来个人识别的公共数据集。使用各种神经网络结构并调整参数，我们已经获得了更令人满意的分类效果。

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