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

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.

Keywords

measurement and control security / radio frequency (RF) fingerprinting / identity identification / deep learning