Adaptive Radar Illuminations with Deep Reinforcement Learning: Illumination Scheduling for Long Range Surveillance Radar with the use of Proximal Policy Optimization
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Data science and AI (MPDSC), MSc
Data science and AI (MPDSC), MSc
Publicerad
2023
Författare
Sandelius, Samuel
Ekelund Karlsson, Albin
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
A modern radar antenna can direct its energy electronically without inertia or the
need for mechanically steering. This opens up several degrees of freedom such as
transmission direction and illumination time, and thus also the potential to optimise
operation in real-time. Long range surveillance radars solve the trade-off between
searching for new targets and tracking known targets. This optimisation is often
rule-based.
In recent years, Reinforcement Learning (RL) Algorithms have been able to efficiently solve increasingly difficult tasks, such as mastering game strategies or solving complex control tasks. In this thesis we show that reinforcement learning can
outperform such rule-based approaches for a simulated radar.
Beskrivning
Ämne/nyckelord
Reinforcement Learning RL, Radar Target Tracking, Partially Observed Markov Decision Process POMDP, Active Electronically Scanned Array Antenna, Airborne Surveillance Radar