The rise of small unmanned aerial systems (sUAS) near airports presents growing safety risks, including mid-air collisions, operational disruptions, and security threats. Current detection systems, su..
The rise of small unmanned aerial systems (sUAS) near airports presents growing safety risks, including mid-air collisions, operational disruptions, and security threats. Current detection systems, such as radar and optical tracking, struggle to reliably identify and classify aerial, particularly non-cooperative drones, under variable operational conditions. This research proposes a multi-modal aerial object detection system that combines multiple sensor modalities (Radiofrequency, Vision, Sound) to enable real-time surveillance. By integrating sensor fusion and machine learning (ML), the system aims to improve detection and classification accuracy, reduce false positives, and support Real-time Decision-making for airport safety personnel. The research aims to evaluate system performance under varied weather and lighting conditions and develop a scalable framework for enhanced situational awareness in airport operations.
