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Volume 32Issue 1
Feb. 2023
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Xiaogang Tang, Junhao Feng, Binquan Zhang, Hao Huan. Radio Frequency Fingerprint-Based Satellite TT&C Ground Station Identification Method[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2023, 32(1): 1-12. doi: 10.15918/j.jbit1004-0579.2022.074
Citation: Xiaogang Tang, Junhao Feng, Binquan Zhang, Hao Huan. Radio Frequency Fingerprint-Based Satellite TT&C Ground Station Identification Method[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2023, 32(1): 1-12.doi:10.15918/j.jbit1004-0579.2022.074
shu

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

doi:10.15918/j.jbit1004-0579.2022.074
  • 1.

    School of Aerospace Information, Space Engineering University, Beijing 101407, China

  • 2.

    School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds:The associate editor coordinating the review of this manuscript was Dr. Na Liu. This work was supported by the National Natural Science Foundation of China (No. 62027801).
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  • Author Bio:

    Xiaogang Tangreceived the M.S. and Ph.D. degrees from Xi’an Jiaotong University, Xi’an, China, in 2005 and 2014, respectively. He is currently an Associate Professor with Space Engineering University. His research interests include pattern recognition and information intelligent processing. He has published several research articles in scholarly journals in the above research areas and has participated in several conferences

    Junhao Fengis currently pursuing a master’s degree in information and communication engineering at Space Engineering University. His research interests are mainly focused on RF fingerprint identification and deep learning

    Binquan Zhangreceived his Ph.D. degree from Xi’an Jiaotong University, China in 2021. He joined the School of Aerospace Information at Space Engineerinng University in 2022. His research interests include intelligent information processing and robotics

    Hao Huanreceived the B.E. degree in information engineering from Zhengzhou University, Zhengzhou, China, in 2006 and the Ph.D. degree in information and communication engineering from Beijing Institute of Technology, Beijing, China, in 2013. He is currently an Assistant Professor in the School of Information and Electronics, Beijing Institute of Technology, Beijing, China. He was a Visiting Researcher at the University of Delaware, Newark, DE, USA in 2017. His research interests include wireless communication, and emitter location

  • Corresponding author:titantxg@163.com
  • Received Date:2022-07-03
  • Rev Recd Date:2022-08-03
  • Accepted Date:2022-09-09
  • Publish Date:2023-02-28
    Abstract
  • 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 signals. The 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 results. The 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 units. Simulation 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 rate. The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.
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