Exploration of Reinforcement Learning in Radar Scheduling

Abstract

The development of phased array antennas has enhanced the effectiveness of radars thanks to it’s flexibility allowing the radar beam to be controlled and adapted almost instantaneously. This flexibility allows a radar to carry out multiple tasks simultaneously, such as surveillance of an area and tracking of targets. Traditionally the scheduling is performed according to hard-coded priority lists in combination with local optimisation, rather than global mathematical optimisation. Reinforcement learning algorithms have in the last few years successfully solved several artificial control tasks and is slowly starting to show some successes in realworld scenarios. Encouraged by the success we study the application of the Proximal Policy Optimisation (PPO) algorithm on a radar scheduling task. The algorithm is trained to track targets and search for new ones within a surveillance area. The proposed algorithm did not solve the scheduling task, but we identify and formalise the challenges that need to be addressed to be able to solve the radar scheduling task with the PPO algorithm.