A Soft-Kill Reinforcement Learning Counter Unmanned Aerial System (C-UAS) With Accelerated Training

In recent years, unmanned aerial vehicles (UAVs) have gained significant popularity and are used for many applications, from entertainment to surveillance and the modern battlefield. As regulation demands arose worldwide, controling and reacting to unauthorized flights of UAVs became a pressing issue. In this work, we present an algorithm to accelerate the training of a reinforcement learning drone agent for a counter unmanned aerial system (C-UAS). The main objective of this C-UAS is to guide an invading drone to a safe-killing zone (SZ) using a hunter quadrotor drone. The hunter quadrotor launches a spoofing, or meaconing, attack on the GNSS receiver of the invading drone. The proposed algorithms employ an abstraction of the C-UAS problem to accelerate the training step and enable training during the mission. Results for different SZ radii are discussed using a software-in-the-loop simulation for ground truth based on a detailed model of the UAV embedded system and flight dynamics, including error metrics and action time. We show that a 99% probability of successful target steering to the SZ can be achieved considering a SZ radius of 75 meters and a Q-table trained with the proposed accelerated training model.

Automated summary

In recent years, the use of unmanned aerial vehicles (UAVs) has become increasingly popular for various purposes. However, the need for regulation and control of unauthorized UAV flights has become a pressing issue. In this study, we propose an algorithm to speed up the training of a reinforcement learning drone agent for a counter unmanned aerial system (C-UAS), which aims to guide an invading drone to a safe-killing zone (SZ) using a hunter quadrotor drone. We conducted simulations to evaluate the performance of the proposed algorithm, and the results indicate that a high probability of successful target steering to the SZ can be achieved.