一种应用于低端接收机的优化射频指纹提取方法

Fangyuan Zhao, Yanhua Jin
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引用次数: 0

摘要

本文重点研究了低端接收机中无线电发射机的分类与识别方法,提供了一种新的射频指纹识别方法。本文提出的基于经验模态分解(EMD)的算法是针对实际射频信号分解中模式混叠问题的一种优化方法。首先,在EMD前进行信号叠加去噪处理,降低信道噪声的影响;然后,采用自相关函数判别法对EMD后的模态分量进行区分。第三,利用盒维数提取特征,形成特征向量作为射频指纹,通过神经网络分类器进行分类,定义信号的发射装置;实验结果表明,该算法在低端信号接收设备USRP中具有良好的识别效果,能够有效识别来自不同对讲机个体的信号,验证了算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Optimized Radio Frequency Fingerprint Extraction Method Applied to Low-End Receivers
This paper focuses on the method for classification and identification of radio transmitters in low-end receivers, to provide a new radio frequency fingerprint. The proposed algorithm based on the Empirical Mode Decomposition (EMD) is an optimized method for the actual radio frequency signal decomposition which will cause the problem of mode aliasing. Firstly, a signal superposition denoising process is applied before EMD to reduce the influence of channel noise. And then, an autocorrelation function discrimination method is performed to distinguish different modal components after EMD. Thirdly, box dimension is used to extract features to form feature vectors as the radio frequency fingerprint which is classified by the neural network classifier, defining the transmitting device of the signal. The experiment results show that the algorithm has a good recognition effect in the low-end signal receiving equipment USRP, and it can effectively recognize the signals from different interphone individuals, which verifies the effectiveness of the algorithm.
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