This paper deals with the automatic classification of Drones using a surveillance radar signal. We show that, using state-of-the-art feature-based machine learning techniques, UAV tracks can be automatically distinguished from other object (e.g. bird, airplane, car) tracks. In fact, on a collection of real data, we measure an accuracy higher than 98%. We have also exploited the possibility of using the same features to distinguish the type of the wing of drone, between Fixed Wing and Rotary Wing, reaching an accuracy higher than 93%.
- Surveillance radar
- Counter unmanned aerial vehicle
- Support Vector Machines