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

This study presents a radio frequency (RF) fingerprint identification method combining a convolutional neural network (CNN) and gated recurrent unit (GRU) network to identify mea-surement 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 algo-rithm 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 man-ufacturer, with fewer parameters and less computation than a network model with the same identi-fication 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.