基于射频指纹的卫星测控地面站身份识别方法

Encryption authentication is generally adopted to ensure the security of traditional satellite Telemetry Track and Command (TT&C). However, several security limitations remain to be improved such as identity counterfeiting and deception. A satellite TT&C ground station identity recognition method via radio frequency fingerprint is presented, and a lightweight convolutional neural network for satellite platforms is designed. Relevant features of the IQ signal are extracted through the convolution layer in the IQ direction, which converts the two-dimensional data to one dimension. The time-domain structural features of the signal are extracted by using the multi-layer convolution in the time-series direction. Then a maximum pooling layer is developed to reduce the data dimension, ensuring that the original feature information contained in the IQ signal is fully utilized and the computation burden is reduced. Finally, the identification of the satellite TT&C ground station is realized by two full connection layers. Simulation experiments show that the average accuracy of the proposed method for 21 transmitters is 93.8%, which is 39.8% higher than the traditional support vector machine method, 11.5% higher than the DLRF network model, and 29.8% higher than the Oracle network model. And the results indicate that the proposed method is robust and requires less computation, which shows the theoretical references and engineering application value for improving the security of the satellite TT&C link.