TY - GEN
T1 - Radio Frequency Fingerprint Identification Based on High-Low Frequency Orthogonal Constraints
AU - Xu, Junfeng
AU - Xiao, Jing
AU - Shao, Zeqi
AU - Ma, Baoquan
AU - Xu, Xingming
AU - Hao, Bowen
AU - Ding, Wenrui
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Radio frequency fingerprinting is an effective technical method for device authentication based on the radiation characteristics of devices. However, the characteristic energy of radio frequency fingerprints is weak and prone to noise interference, which leads to degraded device identification performance under low signal-to-noise ratio (SNR) conditions. To solve this problem, this paper proposes a radio frequency fingerprinting method based on high-low frequency orthogonal constraints, aimed at improving the identification performance of ZigBee devices under low SNR. This method enhances the spectral representation capability of signals by introducing a residual network with high-low frequency grouped convolution, thereby improving the noise robustness of the model. Meanwhile, a learnable high-low frequency filter is designed, and orthogonal constraint loss is used to constrain high-low frequency components. This strengthens the decoupling and discriminability of features, avoids interference between high and low frequency features, and improves the model’s sensitivity to device differences. Experimental results show that the proposed method achieves an identification accuracy of 84.07% on a dataset of ZigBee device signals actually collected in real environments, effectively improving the identification performance.
AB - Radio frequency fingerprinting is an effective technical method for device authentication based on the radiation characteristics of devices. However, the characteristic energy of radio frequency fingerprints is weak and prone to noise interference, which leads to degraded device identification performance under low signal-to-noise ratio (SNR) conditions. To solve this problem, this paper proposes a radio frequency fingerprinting method based on high-low frequency orthogonal constraints, aimed at improving the identification performance of ZigBee devices under low SNR. This method enhances the spectral representation capability of signals by introducing a residual network with high-low frequency grouped convolution, thereby improving the noise robustness of the model. Meanwhile, a learnable high-low frequency filter is designed, and orthogonal constraint loss is used to constrain high-low frequency components. This strengthens the decoupling and discriminability of features, avoids interference between high and low frequency features, and improves the model’s sensitivity to device differences. Experimental results show that the proposed method achieves an identification accuracy of 84.07% on a dataset of ZigBee device signals actually collected in real environments, effectively improving the identification performance.
KW - high-low frequency filters
KW - high-low frequency grouped convolution
KW - Radio frequency fingerprint identification
UR - https://www.scopus.com/pages/publications/105042966549
U2 - 10.1007/978-981-95-8435-2_16
DO - 10.1007/978-981-95-8435-2_16
M3 - 会议稿件
AN - SCOPUS:105042966549
SN - 9789819584345
T3 - Lecture Notes in Electrical Engineering
SP - 185
EP - 199
BT - Proceedings of 2025 9th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Control Technologies
A2 - Wang, Qing
A2 - Dong, Xiwang
A2 - Song, Peng
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2025
Y2 - 31 October 2025 through 3 November 2025
ER -