多模态融合实时识别无人机威胁并预测行为意图
DroneShield-AI: A Multi-Modal Sensor Fusion Framework for Real-Time Autonomous Drone Threat Detection, Behavioral Intent Classification, and Swarm Intelligence in Contested Airspace
- 融合射频、声学、视觉等多源数据，统一处理流程
- 检测准确率96.1%，误报率仅3.2%，延迟142ms
- 支持无人机群战术分析，适合安防与反制场景
无人机威胁已成为21世纪的核心安全挑战。本文提出DroneShield-AI，一个集成六层处理的开源框架：射频信号分类、声学电机特征检测、基于YOLOv8的视觉检测、证据加权传感器融合、行为意图分类引擎（BICE）以及图神经网络无人机群智能模块（GNN-SIM）。BICE首次建立六类无人机飞行模式的系统化威胁分类体系，可实现30秒提前预警。GNN-SIM是首个基于图注意力网络的对抗性多无人机编队分析开源框架。在三个公开真实数据集上评估，融合流水线达到96.1%检测准确率、3.2%误报率、AUC-ROC 0.981，端到端延迟仅142ms，运行于成本约500-780美元的通用CPU硬件。所有代码、模型权重及仿真数据集均已公开。
原文摘要 · Abstract (English)
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). BICE introduces the first systematic six-class threat taxonomy for drone flight patterns, enabling predictive operator alerts with a 30-second advance-warning horizon. GNN-SIM is the first open framework for adversarial multi-drone formation analysis using Graph Attention Networks. Evaluated on three publicly available real-world datasets, the fused pipeline achieves 96.1% detection accuracy, 3.2% false alarm rate, AUC-ROC: 0.981, and 142ms end-to-end latency on commodity CPU-class hardware at approximately $500-$780 USD total system cost. All code, model weights, and simulation datasets are publicly released at submission.
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