Securing your Airspace: Detection of Drones Trespassing Protected Areas
Alireza Famili, Angelos Stavrou, Haining Wang, Jung-Min (Jerry) Park,, Ryan Gerdes

TL;DR
This paper surveys various drone detection methods using radar, acoustic, optical, and RF sensors, highlighting their strengths and weaknesses, and suggests that multimodal approaches could improve real-time security in protected areas.
Contribution
It provides a comprehensive comparison of existing drone detection techniques and discusses the potential benefits of combining multiple sensing modalities.
Findings
Radar and RF methods offer high accuracy but can be costly.
Acoustic and optical sensors are more affordable but less reliable in complex environments.
Multimodal detection approaches may enhance overall detection performance.
Abstract
There has been a rapid growth in the deployment of Unmanned Aerial Vehicles (UAVs) in various applications ranging from vital safety-of-life such as surveillance and reconnaissance at nuclear power plants to entertainment and hobby applications. While popular, drones can pose serious security threats that can be unintentional or intentional. Thus, there is an urgent need for real-time accurate detection and classification of drones. In this article, we perform a survey of drone detection approaches presenting their advantages and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost, concluding that combining multiple sensing modalities might be the path forward.
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Taxonomy
TopicsUAV Applications and Optimization · Robotics and Sensor-Based Localization · Remote Sensing and LiDAR Applications
