Exploration of Reinforcement Learning in Radar Scheduling
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2021
Författare
Nathanson, Axel
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
radar scheduling, reinforcement learning, PPO, machine learning