Radio Frequency Fingerprint Recognition Method Based on Feature Fusion and Enhanced Residual Networks

Authors:
Publisher website
Google Scholar
Abstract
Radio Frequency Fingerprint Identification (RFFI) is a technology that utilizes unique hardware characteristics for identity authentication. This paper proposes a feature fusion-based RFFI approach employing an enhanced residual network (ResNet) to improve recognition accuracy under various signal-to-noise ratio (SNR) conditions. First, pulse sequences are extracted, and the following features are then constructed: ambiguity function (AF), time-frequency spectrograms (TFS), bispectral analysis (BIS), and intrinsic function-phase feature (IFP). The network architecture is based on an enhanced ResNet with a multi-path structure, gated recurrent unit (GRU), and attention mechanisms. We ultimately validate our approach using real-world automatic dependent surveillance-broadcast (ADS-B) data. Experimental results reveal the following key findings: 1) For the original signal with an SNR of 25 dB, the accuracy of the method stacking four features improves by 4.55% compared to the method that stacks two features. 2) By leveraging the AF feature, a notable accuracy gain of 24.3% is achieved by the enhanced ResNet compared to the classical ResNet-34. 3) Employing a combination of four features with the enhanced ResNet, this approach yields a 31.81% accuracy improvement over the method that uses AF with ResNet-34. 4) With knowledge distillation applied, the ShuffleNet-V2, using ResNet-G as the teacher model for distillation, maintains a parameter size of only 2.28M, while suffering a mere 1.33% accuracy loss.
Funding Information
  • National Natural Science Foundation of China (62271079)
  • National Natural Science Foundation of China (61875239)
  • National Natural Science Foundation of China (62127802)
  • State Key Laboratory of Integrated Services Networks, Xidian University
  • National Key Research and Development Program of China (2018YFB2200903)
  • National Key Research and Development Program of China (2023YFB2905600)
  • Beijing Nova Program, China (Z211100002121138)

This publication has 37 references indexed in Scilit: