Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone

Abstract

Unmanned aerial vehicles (UAVs) are fast becoming a low-cost, affordable tool for various security and surveillance tasks. It has led to the use of UAVs (drones) for unlawful activities such as spying or infringing on restricted or private air spaces. This rogue use of drone technology makes it challenging for security agencies to maintain the safety of many critical infrastructures. Additionally, because of the drones’ varied low-cost design and agility, it has become challenging to identify and track them using conventional radar systems. This paper proposes a deep reinforcement learning-based approach for identifying and tracking an intruder drone using a chaser drone. Our proposed solution employs computer vision techniques interleaved with a deep reinforcement learning control for tracking the intruder drone within the chaser’s field of view. The complete end-to-end system has been implemented using robot operating system and Gazebo, with an Ardupilot-based flight controller for flight stabilization and maneuverability. The proposed approach has been evaluated on multiple dynamic scenarios of intruders’ trajectories and compared with a proportional-integral-derivative-based controller. The results show that the deep reinforcement learning policy achieves a tracking accuracy of 85%. The intruder localization module is able to localize drones in 98.5% of the frames. Furthermore, the learned policy can track the intruder even when there is a change in the speed or orientation of the intruder drone.