Radio Frequency Fingerprint Recognition Method Based on Adaptive Energy Path Integrated Projection and Trans-MAML Method

In wireless communications, radio frequency fingerprint identification (RFFI) leverages unique hardware characteristics for device recognition. This paper proposes an innovative few-shot RFFI method based on adaptive energy path integrated projection (AEP-IP) and an enhanced neural network, aiming to significantly improve recognition accuracy under various signal-to-noise ratio (SNR) conditions. The method integrates an enhanced densely connected temporal convolutional network (DenseTCN), transfer learning, and Meta-Learning (MAML). Key steps include extracting pulse sequences from received signals and constructing four features: ambiguity function (AF), time-frequency spectrogram (TFS), bispectrum analysis (BIS), and intrinsic function-phase (IFP). One-dimensional projection features are extracted through these features. The DenseTCN enables cross-layer feature fusion and serves as the backbone of the Trans-MAML scheme to enhance few-shot learning under low SNR conditions. Validated using real-world automatic dependent surveillance-broadcast (ADS-B) data, the results demonstrate: (1) Four-timestep feature improve accuracy by 7.9% (25 dB SNR) and 11% (0 dB SNR) compared to two-timestep feature. (2) DenseTCN outperforms TCN by 8.86% under the same conditions. (3) DenseTCN achieves comparable performance to TCN with 100% data using only 20% data (25 dB SNR). (4) With limited training data, the Trans-MAML method achieves higher recognition accuracy across all SNR levels compared to baseline methods.