基于深度学习的通信发射器个体识别

Jie Xu, Weiguo Shen, Wei Wang
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引用次数: 1

摘要

针对通信发射机的个体识别问题,本文从通信发射机在信号层的细微特征出发,提出了一种基于深度学习的个体识别方法。首先,建立了基于深度学习的识别框架,设计了包含两层隐藏层的卷积神经网络,通过两层卷积运算提取局部特征;其次,采用随机梯度下降法对参数进行优化,并采用软最大值模型确定输出标号;最后,通过实验验证了该方法的有效性。
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Individual Recognition of Communication Emitter Based on Deep Learning
In view of the individual recognition problem of the communication emitter, this paper, starting with the subtle characteristics of the communication emitter in the signal layer, proposes a method of individual recognition based on deep learning. First, a recognition framework based on deep learning is established, and a convolution neural network containing two hidden layers is designed to extract local features through two layers convolution operations. Secondly, the stochastic gradient descent method is used to optimize the parameters, and the soft max model is used to determine the output label. Finally, the effectiveness of the method is verified by experiments.
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