The rapid proliferation of low-cost unmanned aerial vehicles (UAVs) has created critical vulnerabilities in airspace security at airports, government installations, and military facilities. Traditional radar-based detection systems exhibit a fundamental visibility gap, as they are optimized for large aircraft rather than small, low-altitude drones. This paper presents a technical and methodological analysis of a Smart Drone Defense System that integrates AI-powered computer vision using the YOLOv11 object detection framework with Full-Duplex Soft- ware Defined Radio (SDR) technology for simultaneous signal jamming. The proposed integrated architecture eliminates the detection blind spot inherent in conventional jamming systems, achieves drone identification in under 50 milliseconds, and improves radio blocking efficiency by 40%. Comparative analysis against traditional and single-modality systems demonstrates superior accuracy and response time, establishing the viability of multi-modal smart systems for next-generation airspace protection.
Shah et al. (Thu,) studied this question.
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