# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 3 Sensing System  
The perception of a threat makes use of the sensing system, consisting of one or more sensors capable of collecting the information extrapolated from the electromagnetic or acoustic spectrum, depending on the technology and the signal processing involved. In general, the perception operation can be divided into the following phases:  
* Detection: The finding of one or more object within the airspace to be monitored. In this first phase, the system is not yet able to distinguish whether the detected object is actually a drone. This phase can be characterized through the two indicators \"Detection Rate\" and \"False Alarm Rate\", which express the probability, respectively, of correct detection and false alarm.
* Classification: Once the detection event has occurred, it is necessary to verify that the detected object is actually present and that it is a drone. It could happen, for example, that the target detected in the previous phase is a bird, which has electromagnetic characteristics that can be similar to those of a drone (the radar cross section or the size and geometric shape that is possible recognize visually). This verification is also called \"recognition\" or \"identification\". Subsequently, the system extrapolates some salient attributes (features) of the drone, such as the type (size, type of propulsion, number of rotors, model), the possible location of a remote pilot, the presence of a payload and its typology. This phase may be found in the literature under the term \"identification\".
* Localization/Tracking: The target is located by estimating its position in terms of angle and distance. Triangulation techniques can be used to increase accuracy. Once the target has been locked in, it must be tracked throughout its flight. Flight trajectory could also be predicted.  
The level of reliability of this information must be as high as possible so that the C2 system can perform the threat analysis and select and adopt the most appropriate mitigation measures in the shortest time interval. Detection, recognition and identification (or classification) could be performed by a single type of sensor if the technology and associated processing are compatible with the required output. Where this is not the case, it is possible to adopt a heterogeneous sensing system consisting of sensors with different technologies, which can contribute, thanks to data/sensor fusion techniques, to obtain a reliable level of identification and to improve performance in terms of range, detection time of the anti-drone system, detection rate and reduction of false alarms.  
Clearly, to face an attack by a team or swarm of drones, the sensing system should be enriched with functionalities for the perception and the processing of information about the features that are strictly related to the teaming or swarming behavior of the attacker.  
In more detail, one of the most complex scenarios that an anti-drone system can face is that relating to the attack of a drone swarm, e.g., for saturation attacks and to overwhelm the counter-capability of the target's defense. As for a hostile team, this scenario requires that the detection capabilities are also used for the extraction of \"global\" features that characterize the swarm and not only for the \"local\" features related to the drones that compose it. Moreover, specific information should be collected to quantify swarming metrics (e.g., consistency, cohesion, etc.) related to the swarming behavior. For this purpose, it may be useful to acquire both local information (e.g., flight configuration, geometry characteristics and speed) and global information (e.g., number of vehicles, relative distances, geometry of the swarm, etc.) [30]. Such information will be essential to infer the mitigation decisions, since they may support the identification of the drones that represent the \"focal points\" within the hostile swarm.