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.
Pour en savoir plus : LUMEN – A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices
