Adaptive Radar Illuminations with Deep Reinforcement Learning: Illumination Scheduling for Long Range Surveillance Radar with the use of Proximal Policy Optimization

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dc.contributor.authorSandelius, Samuel
dc.contributor.authorEkelund Karlsson, Albin
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerHelgesson, Peter
dc.contributor.supervisorAndersson, Adam
dc.date.accessioned2023-08-28T07:27:11Z
dc.date.available2023-08-28T07:27:11Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractA 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.
dc.identifier.coursecodeMVEX03
dc.identifier.urihttp://hdl.handle.net/20.500.12380/306947
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectReinforcement Learning RL, Radar Target Tracking, Partially Observed Markov Decision Process POMDP, Active Electronically Scanned Array Antenna, Airborne Surveillance Radar
dc.titleAdaptive Radar Illuminations with Deep Reinforcement Learning: Illumination Scheduling for Long Range Surveillance Radar with the use of Proximal Policy Optimization
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
local.programmeData science and AI (MPDSC), MSc
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