Satellite Telemetry Track and Command Ground Station Identification Method Based on RF Fingerprint

TANG Xiaogang; FENG Junhao; ZHANG Binquan; HUAN Hao; REN Yanjie; LI Haibin

doi:10.11999/JEIT220804

Volume 45 Issue 7

Jul. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(7): 2554-2560

TANG Xiaogang, FENG Junhao, ZHANG Binquan, HUAN Hao, REN Yanjie, LI Haibin. Satellite Telemetry Track and Command Ground Station Identification Method Based on RF Fingerprint[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2554-2560. doi: 10.11999/JEIT220804

Citation:

TANG Xiaogang, FENG Junhao, ZHANG Binquan, HUAN Hao, REN Yanjie, LI Haibin. Satellite Telemetry Track and Command Ground Station Identification Method Based on RF Fingerprint[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2554-2560. doi: 10.11999/JEIT220804

Citation:

TANG Xiaogang, FENG Junhao, ZHANG Binquan, HUAN Hao, REN Yanjie, LI Haibin. Satellite Telemetry Track and Command Ground Station Identification Method Based on RF Fingerprint[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2554-2560. doi: 10.11999/JEIT220804

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Satellite Telemetry Track and Command Ground Station Identification Method Based on RF Fingerprint

doi: 10.11999/JEIT220804

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 National Natural Science Foundation of China (62027801)

Received Date: 2022-06-17
Accepted Date: 2022-10-08
Rev Recd Date: 2022-09-13

Available Online: 2022-10-14

Publish Date: 2023-07-10

Abstract

Abstract

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.
- Telemetry Track and Command (TT&C) security,
- RF fingerprinting,
- Identity recognition,
- Deep learning

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References(14)

References

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