{"id":57852,"date":"2026-05-26T09:29:32","date_gmt":"2026-05-26T07:29:32","guid":{"rendered":"https:\/\/www.nae.fr\/2026\/05\/26\/lumen-a-lightweight-uav-multi-enhanced-network-for-psd-based-rf-fingerprinting-on-edge-devices\/"},"modified":"2026-05-26T09:29:32","modified_gmt":"2026-05-26T07:29:32","slug":"lumen-a-lightweight-uav-multi-enhanced-network-for-psd-based-rf-fingerprinting-on-edge-devices","status":"publish","type":"post","link":"https:\/\/www.nae.fr\/en\/2026\/05\/26\/lumen-a-lightweight-uav-multi-enhanced-network-for-psd-based-rf-fingerprinting-on-edge-devices\/","title":{"rendered":"LUMEN: A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices"},"content":{"rendered":"
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\n\nUnmanned 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.\n\n<\/div>\n<\/div><\/blockquote>\nPour en savoir plus :\u00a0LUMEN – A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices<\/a>","protected":false},"excerpt":{"rendered":"

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 […]<\/p>\n","protected":false},"author":1,"featured_media":55241,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[34,16],"tags":[35,44,33],"class_list":["post-57852","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovation-et-technologique","category-rti","tag-actualites","tag-developpement-des-systemes-intelligents","tag-drones"],"acf":[],"yoast_head":"\nLUMEN: A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices - NAE<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.nae.fr\/en\/2026\/05\/26\/lumen-a-lightweight-uav-multi-enhanced-network-for-psd-based-rf-fingerprinting-on-edge-devices\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LUMEN: A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices - NAE\" \/>\n<meta property=\"og:description\" content=\"Unmanned aerial vehicle (UAV) identification in edge environments requires both high classification accuracy and efficient real-time deployment on lightweight hardware. 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