AI-Driven Distributed Multi-Sensor Fusion for Real-Time Drone Detection in Urban Airspace

Abstract

The increasing density of unmanned aerial systems (UAS) in urban low-altitude airspace introduces significant safety and security challenges, particularly for detecting non-cooperative drones in environments where RADAR (Radio Detection and Ranging) deployment is impractical. This paper presents a distributed, artificial intelligence (AI)-enabled multi-sensor surveillance framework integrating visual, acoustic, and radio frequency (RF) sensing through weighted decision-level fusion.Each sensing modality is processed using dedicated deep learning models, while a decision-level fusion mechanism combines predictions based on confidence scores and reliability weights. The modular architecture enables asynchronous communication through a publisher–subscriber paradigm, supporting distributed deployment and resilience under partial sensor degradation.The system is evaluated through both laboratory experiments and simulated urban environments with varying complexity. Results demonstrate that the proposed framework achieves over 90% detection precision, maintains false positive rates below 10%, and supports real-time processing exceeding 50 Hz. Furthermore, the fusion strategy effectively mitigates performance degradation in individual sensing modalities, particularly under noisy or obstructed conditions.These results highlight the potential of AI-based distributed sensor fusion systems as a scalable and cost-effective solution for real-time drone surveillance in smart urban airspace, contributing to resilient monitoring within emerging U-space ecosystems.