Radio Frequency Fingerprinting and Ascend Deployment Based on Multi-Domain Characteristics of UAV Signals

The rapid proliferation of drones has raised growing concerns regarding low-altitude airspace safety. In practical UAV communication scenarios, relying solely on a single time-domain IQ signal is often insufficient to fully characterize UAV RF fingerprints. Moreover, increasingly subtle hardware differences among UAV transmitters make RF fingerprint identification more challenging. Other representations, such as STFT-based features, are commonly converted into image-like inputs for neural networks, increasing deployment complexity on edge devices. To address these challenges, this paper proposes a UAV recognition framework based on multi-domain signal representations. The proposed framework employs a multi-domain input strategy and structural reparameterization to reduce the number of parameters, computational cost, and deployment latency. Experiments under AWGN conditions demonstrate that the proposed model achieves superior recognition performance in both UAV classification and individual identification tasks. The proposed model is further deployed on the Ascend 910B platform to verify its deployment feasibility.