This work considers the problem of intercepting rogue drones targeting sensitive critical infrastructure facilities. While current interception technologies focus mainly on the jamming/spoofing tasks, the challenges of effectively locating and tracking rogue drones have not received adequate attention. Solving this problem and integrating with recently proposed interception techniques will enable a holistic system that can reliably detect, track, and neutralize rogue drones. Specifically, this work considers a team of pursuer UAVs that can search, detect, and track multiple rogue drones over a sensitive facility. The joint search and track problem is addressed through a novel multiagent reinforcement learning scheme to optimize the agent mobility control actions that maximize the number of rogue drones detected and tracked. The performance of the proposed system is investigated under realistic settings through extensive simulation experiments with varying number of agents demonstrating both its performance and scalability.

The wide adoption and use of unmanned aerial vehicles (UAVs) has created not only opportunities but also threats to the security of sensitive areas. Thus, effective and efficient counter-drone systems are required to protect these areas. This work tackles this issue by developing cooperative multi-agent jamming techniques using reinforcement learning (RL) to counter the operation of one or multiple rogue drones flying over a sensitive area. The aim of the proposed RL approach is to optimize the joint mobility and power control actions of the pursuer UAVs in order to maximize the received jamming power at the rogue drones aiming at disrupting communication links and sensing circuitry, while at the same time keeping the interference to surrounding pursuer agents below a predefined threshold. The effectiveness of the proposed approach in terms of scalability, learning speed, and agents' final joint performance is demonstrated through extensive simulation experiments for various agent and target configurations.

Drones, including remotely piloted aircraft or unmanned aerial vehicles, have become extremely appealing over the recent years, with a multitude of applications and usages. However, they can potentially present major threats for security and public safety, especially when they fly across critical infrastructures and public spaces. This work investigates a novel counter-drone solution by proposing a multi-agent framework in which a team of pursuer drones cooperate in order to track and jam a rogue drone. Within the proposed framework, a joint mobility and power control solution is developed to optimize the respective decisions of each cooperating agent in order to best track and intercept the moving rogue drone. Both centralized and distributed variants of the joint optimization problem are developed and extensive simulations are conducted to evaluate the performance of the problem variants and to demonstrate the effectiveness of the proposed solution.

Over the last few years there has been an unprecedented interest in unmanned aerial vehicles (UAVs). However, drones potentially pose great threats to security and public safety, especially when their malicious use involves critical infrastructures and public spaces. This work proposes a multiagent counter-drone system where a team of pursuer drones cooperate in order to track and jam multiple rogue drones. Specifically, a cooperative multi-agent approach is proposed in which the best joint mobility and power control actions of each agent are chosen so that the rogue drones are optimally tracked and jammed over time. Two variants of the joint optimization problem are developed and extensive simulations are conducted so as to evaluate the performance of the proposed approach.