基于射频信号特征的Airmax设备指纹提取方法

Fingerprint Extraction Method of Airmax Equipment Based on Radio Frequency Signal Characteristics

针对私有协议的Airmax设备,提出了一种新的射频指纹提取方法。首先,介绍了软硬件实验环境的搭建并简要介绍了Airmax技术,然后介绍了帧前导信号的提取方法,分为粗定位和精确定位,接着从理论分析和实验验证阐述了Airmax射频指纹的提取方法。提取的特征维数为14,其中频率偏移相关的特征有2个,幅度相关的特征有12个。最后,基于这14维特征使用K-means算法及决策树模型对设备特征数据集进行了训练和分类,计算了分类准确率,两个模型的准确率都达到了100%,对于4个设备的分类问题,K-means算法的准确率为92.4%,决策树模型的准确率为100%。

A new radio frequency(RF)fingerprint extraction method is proposed for Airmax devices with proprietary protocols.Firstly,the construction of software and hardware experimental environment and the Airmax technology are introduced.Then the extraction method of the frame preamble signals is introduced,which is divided into two rough positioning and precise position.And the extraction method of Airmax RF fingerprint is expounded from theoretical analysis and experimental verification.A total of 14 dimensional features are extracted,in which 2 features are related to the frequency and 12 features are related to the amplitude.Finally,based on the 14-dimensional features,the K-means algorithm and the decision tree model are used to train and classify the data of features,and the classification precision is calculated.The precision of both models reach 100%.For the classification problem of four devices,the precisions of the K-means algorithm and the decision tree model are 92.4% and 100%,respectively.