LUMEN: A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices

Unmanned aerial vehicle (UAV) identification in edge environments requires both high classification accuracy and efficient real-time deployment on lightweight hardware. This study presents LUMEN, a Lightweight UAV Multi-Enhanced Network designed for resource-constrained single-board computer platforms. To enable efficient edge deployment, the proposed method adopts a power spectral density (PSD)-based signal representation together with a lightweight neural network architecture. LUMEN combines multi-channel PSD stacking with multi-scale feature extraction to capture both short-term spectral variations and multi-resolution RF patterns. The proposed pipeline covers UAV RF data collection, including UAV RF data collection, dataset construction, preprocessing, model design, comparative evaluation, and deployment on an RK3582-based edge platform. In the classification experiments, LUMEN achieved the best performance among the four evaluated models, reaching an accuracy of 0.975, compared with 0.775, 0.876, and 0.942 for the baseline models. In the edge deployment test, the model maintained an average inference latency of 1.73 ms and a throughput of 578.95 FPS during a 30-min continuous run, while showing low CPU utilization, low memory usage, and stable thermal behavior. These results demonstrate that LUMEN achieves a practical balance between identification accuracy and runtime efficiency for real-time UAV identification on edge devices.