# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 1 Introduction  
In recent years, the drone market has had a significant expansion, especially in the consumer sector. Drones destined for this market are easily accessible thanks to their relatively low cost. In addition, the characteristics of weight, size, and the ability to carry a payload, such as a camera, allow them to be used in various fields, from the recreational to the professional sector. In addition, from a research point of view, the use of these flying platforms helps the development of technologies whose applications have a positive impact on the community, such as search and rescue operations, intelligent logistics, environmental monitoring or precision agriculture. Given the increasing use of these technologies, the issues related to safety, security and privacy must be taken into consideration. Their use could cause damage to the community due to failures and improper or criminal use. A significant increase has been observed in the number of accidents involving drones or unmanned aerial systems (UAS) [1]. For example, improper use in the vicinity of an airport can represent a serious threat to public safety and a source of discomfort, as evidenced by the hundreds of flights canceled at London Gatwick airport in a few months of 2018 [2]. For this reason, the development of technologies for the detection, identification and mitigation of malicious drones has become of primary importance. A countermeasure system, also called a counter-UAS (C-UAS) or counter-UAS system (CUS), can identify and neutralize an intruder drone classified as a threat. From an architectural point of view, an anti-drone system generally consists of the following fundamental sub-systems:  
* Sensing system;
* Mitigation system;
* Command and control (C2) system. The sensing system consists of one or more sensors capable of collecting information from the surrounding environment. The mitigation system consists of one or more mitigating elements capable of disabling, destroying or taking control of the drone identified as a threat. The C2 system collects data from sensors and executes detection algorithms, based on which it establishes the presence of a threat, identifies it (i.e., classifies its entity) and decides the most appropriate tracking and mitigation mode. There are several C-UAS systems on the market. There are integrated systems that implement both the detection part and the mitigation part on the same platform, but the most adopted solution is to separate the mitigation part from the sensing one, distributing it on different platforms, giving rise to distinct commercial products. For example, most of the available solutions are ground-based, especially for the sensing part, while the sky-based part is typically relegated to mitigation. Thus, a single platform may implement only some of the sub-systems of the CUS (or part of them) and a network architecture is required to implement the interactions between the platforms. In this paper, a multiplatform CUS, based mainly on a team of mini drones acting as a cooperative defensive system, has been used as a reference. Indeed, mini drones represent an effective solution for the implementation of a CUS, being the ideal platforms for the proximal sensing and tracking of moving targets (e.g., intruder drones) in high-mobility scenarios. Moreover, a team of mini drones may be arranged as a mobile sensor network: on the one hand, the single drones may act as mobile sensor nodes to keep the closeness with moving targets; on the other hand, a cooperative behavior may be established by means of the network of drones and a suitable coordination protocol. Such cooperative behavior may ensure the simultaneous perception and tracking of different moving targets, and may provide efficient coverage by balancing the load of the sensing and tracking tasks amongst the sensor nodes. In the end, defensive drones may also be equipped to implement proper neutralization actions with respect to intruder drones.