PFG-DSFNet: Heterogeneous Dual-Stream Frequency-Domain Fusion for Fine-Grained UAV RF Identification

UAV radio-frequency (RF) signal identification is an important technique for low-altitude security monitoring. However, background noise interference and distortions in amplitude and phase features can reduce the robustness of methods based on a single frequency-domain representation. A one-dimensional power spectrum alone provides limited capability for characterizing pairwise relationships among frequency components, whereas two-dimensional feature representations may weaken spectral-line continuity. To address these limitations, this paper proposes a Power Spectral Density-Frequency-domain Gramian Angular Field Dual-stream Fusion Network (PFG-DSFNet). First, each RF observation window is converted into a power spectral density (PSD) sequence and further mapped to a frequency-domain Gramian Angular Field (F-GAF) image. Then, the PSD sequence and F-GAF image are used to extract the spectral envelope and heterogeneous two-dimensional frequency-domain features, respectively. Furthermore, an Adaptive Gated Low-rank Bilinear Interaction (AGLRBI) module is designed for the dual-stream features to model second-order interactions and perform adaptive fusion. In this way, the dual-stream representation framework enhances high-accuracy UAV RF signal identification. Experiments on the DroneRF 10-class classification task show that PFG-DSFNet achieves an accuracy of 99.35% and a Macro-F1 score of 0.9934. Ablation experiments further show that the bilinear interaction and adaptive gating components each contribute to the final performance, and their combination achieves the best result. Additional experiments under mixed-SNR conditions and in measured scenarios further support the robustness of the proposed method.