In recent years, the drone market has had a significant expansion, with applications in various fields (surveillance, rescue operations, intelligent logistics, environmental monitoring, precision agriculture, inspection and measuring in the construction industry). Given their increasing use, the issues related to safety, security and privacy must be taken into consideration. Accordingly, the development of new concepts for countermeasures systems, able to identify and neutralize a single (or multiples) malicious drone(s) (i.e., classified as a threat), has become of primary importance. For this purpose, the paper evaluates the concept of a multiplatform counter-UAS system (CUS), based mainly on a team of mini drones acting as a cooperative defensive system. In order to provide the basis for implementing such a system, we present a review of the available technologies for sensing, mitigation and command and control systems that generally comprise a CUS, focusing on their applicability and suitability in the case of mini drones.

Drone expansion needs to be considered as a menace in cases of negligent, illicit, or non-cooperative use. In the case of airports, a complete protection against drone intrusion should rely on an intrusion management system, aiming at avoiding the closure of the airport. This system requires the setting of proper risk assessment methodologies for airport operations, to explicitly consider the features of drone intrusion, possibly from a quantitative point of view. This work proposes a methodological framework for the risk assessment of drone intrusions in airports, tailored on drone-intrusion features, airport features, and current operations, and considering both safety-related and security-related causes. The framework is based on the combination of model-based and data-driven approaches in order to: (i) estimate an airport vulnerability index, to measure the susceptibility of the airport to drone intrusions, based on reference datasets; (ii) specify a set of event trees to evaluate the risks of the different threat scenarios related to drone intrusions. The proposed methodological framework is applied to a concrete case study, related to Milan Malpensa airport. The achieved results show the effectiveness of the approach and elicit further requirements for counter-drone systems in airports based on the assessed risks.

The problem of generating optimal flight trajectories for an unmanned aerial vehicle in the presence of no-fly zones is computationally expensive. It is usually solved offline, at least for those parts which cannot satisfy real time constraints. An example is the core paths graph algorithm, which discretizes the operational flight scenario with a finite dimensional grid of positions-directions pairs. A weighted and oriented graph is then defined, wherein the nodes are the earlier mentioned grid points and the arcs represent minimum length trajectories compliant with obstacle avoidance constraints. This paper investigates the exploitation of two parallel programming techniques to reduce the lead time of the core paths graph algorithm. The former employs some parallelization techniques for multi-core and/or multi-processor platforms. The latter is targeted to a distributed fleet of unmanned aerial vehicles. Here the statement of the problem and preliminary development are discussed. A two-dimensional scenario is analysed by way of example to show the applicability and the effectiveness of the approaches.

In the context of aerospace engineering, the optimization of processes may often require to solve multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities, possibly involving highly-expensive objective function evaluations. In Air Traffic Management (ATM), the optimization of procedures and protocols becomes even more complicated, due to the involvement of human controllers, which act as final decision points in the control chain. In this article, we propose the use of computational intelligence techniques, such as Agent-Based Modelling and Simulation (ABMS) and Evolutionary Computing (EC), to design a simulation-based distributed architecture to optimize control plans and procedures in the context of ATM. We rely on Agent-Based fast-time simulations to carry out offline what-if analysis of multiple scenarios, also taking into account human-related decisions, during the strategic or pre-tactical phases. The scenarios are constructed using real-world traffic data traces, while multiple optimization variables governed by an EC algorithm allow to explore the search space to identify the best solutions. Our optimization approach relies on ad-hoc multi-objective performance metrics which allow to assess the goodness of the control of aircraft and air traffic regulations. We present experimental results which prove the viability of our approach, comparing them with real-world data traces, and proving their meaningfulness from an Air Traffic Control perspective.