High Power Laser and Particle Beams, Volume. 36, Issue 4, 043019(2024)
Visual analysis method for RF fingerprint based on important region localization and masking
A Grad-CAM based visualizing method for important regions is proposed for the interpretability of RF fingerprint extraction and deep learning models of time-domain pulse signal samples. The impact of important regions on RF fingerprint recognition results is analyzed through multiple mask tests of important regions. Based on signal samples of 10 emitters, the test results of two ResNet models with different layers are compared. It is found that the proposed method can distinguish different types of signals and present individual differences. Analysis shows that this method can detect important regional localization differences when different emitters send the same signal, and can visually reflect the spatial distance of RF fingerprint characteristics, as well as the differences in feature representation and fingerprint localization accuracy of different models; At the same time, it is found that masks for important areas are more prone to false predictions, which proves the existence of RF fingerprints related to time-frequency characteristics in specific signals, and can assist in visualizing key points that affect the recognition of RF fingerprint samples.
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Wenbin Liu, Pingzhi Fan, Jiahuang Yang, Yukai Li, Yuhao Wang, Hua Meng. Visual analysis method for RF fingerprint based on important region localization and masking[J]. High Power Laser and Particle Beams, 2024, 36(4): 043019
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Received: Oct. 30, 2023
Accepted: Jan. 22, 2024
Published Online: Apr. 22, 2024
The Author Email: Meng Hua (menghua@swjtu.edu.cn)