LATIDIA · Robótica

DroneShield-AI: un marco de fusión de sensores multimodal para la detección de amenazas de drones autónomos en tiempo real, la clasificación de intención de comportamiento y la inteligencia de enjambre en el espacio aéreo disputado

arXiv:2606.11687v2 Announce Type: replace-cross Resumen: Las amenazas de vehículos aéreos no tripulados (UAV) han surgido como un desafío de seguridad definitorio del siglo XXI. Este documento presenta DroneShield-AI, un marco abierto unificado

La noticia

arXiv:2606.11687v2 Announce Type: replace-cross Abstract: Unmanned Aerial Vehicle (UAV) threats have emerged as a defining security challenge of the 21st century. This paper presents DroneShield-AI, a unified open framework integrating six processing layers: RF signal classification, acoustic motor-signature detection, YOLOv8-based visual detection, evidence-weighted sensor fusion, a Behavioral Intent Classification Engine (BICE), and a Graph Neural Network Swarm Intelligence Module (GNN-SIM). This v2 revision reports measured results on the completed implementation (495 automated tests), superseding the v1 simulation-only preprint. On real public data: RF presence detection reaches F1 0.9924; acoustic detection reaches 98.12% accuracy, though a 4-feature statistical baseline reaches 93.25% on the same split, so the model's drone-specific contribution is