The proliferation of drones for civilian and military usages represents a significant - and increasing - challenge to airspace surveillance radars. Indeed, a great number of UASs has properties that make them hard to detect and difficult to identify. That is the case with small-sized UASs, typically with dimensions lower than 2 meters, made of composite materials which can take-off at very short range from their target’s location, fly at low altitude, at low speed, in a swarm and can have also dynamic radar signatures very close from those of birds.
To date, preliminary promising results were obtained in UAS identification by SET180 TG from simulated electromagnetic signatures and real UAS radar signatures collected during several in-doors and one in-flight measurement campaigns (Spain CEDEA 2013: UNDINE). However, those results relied mainly on techniques developed for fighter a/c recognition. The next step is therefore to adapt or to rethink UASs classification/identification by radars taking into account the specificities of that potential threat, especially its capacity to behave as endo clutter target, to get higher score of correct identification and to reduce false alarm rate.
This effort will build on SET 180 database and developments on classification approaches, for increased situational awareness of the low airspace and ground environment. This effort will include classification on unexpected possible threats amongst UAS and ground vehicles against other competing radar contacts such as birds, dismounts, motorcycles, bikes, and small vehicles (cars, trucks, etc).