Radio Frequency Fingerprint-Based Satellite TT&C Ground Station Identification Method

作     者：Xiaogang Tang Junhao Feng Binquan Zhang Hao Huan Xiaogang Tang;Junhao Feng;Binquan Zhang;Hao Huan

作者机构：School of Aerospace InformationSpace Engineering UniversityBeijing 101407China School of Information and ElectronicsBeijing Institute of TechnologyBeijing 100081China 

出 版 物：《Journal of Beijing Institute of Technology》 (北京理工大学学报（英文版）)

年 卷 期：2023年第32卷第1期

页      面：1-12页

核心收录：

学科分类：0810[工学-信息与通信工程] 080904[工学-电磁场与微波技术] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 081001[工学-通信与信息系统] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(No.62027801) 

主　　题：measurement and control security radio frequency(RF)fingerprinting identity identification deep learning 

摘      要：This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control *** proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification *** number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic *** experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification *** algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.