On the Value of Online Learning for Cognitive Radar Waveform Selection

dc.contributor.authorThornton III, Charles Ethridgeen
dc.contributor.committeechairBuehrer, Richard M.en
dc.contributor.committeememberRuohoniemi, John Michaelen
dc.contributor.committeememberPalsson, Eyvindur Arien
dc.contributor.committeememberMartone, Anthony F.en
dc.contributor.committeememberDhillon, Harpreet Singhen
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2023-05-17T08:00:40Zen
dc.date.available2023-05-17T08:00:40Zen
dc.date.issued2023-05-16en
dc.description.abstractModern radar systems must operate in a wide variety of time-varying conditions. These include various types of interference from neighboring systems, self-interference or clutter, and targets with fluctuating responses. It has been well-established that the quality and nature of radar measurements depend heavily on the choice of signal transmitted by the radar. In this dissertation, we discuss techniques which may be used to adapt the radar's waveform on-the-fly while making very few a priori assumptions about the physical environment. By employing tools from reinforcement learning and online learning, we present a variety of algorithms which handle practical issues of the waveform selection problem that have been left open by previous works. In general, we focus on two key challenges inherent to the waveform selection problem, sample-efficiency and universality. Sample-efficiency corresponds to the number of experiences a learning algorithm requires to achieve desirable performance. Universality refers to the learning algorithm's ability to achieve desirable performance across a wide range of physical environments. Specifically, we develop a contextual bandit-based approach to vastly improve the sample-efficiency of learning compared to previous works. We then improve the generalization performance of this model by developing a Bayesian meta-learning technique. To handle the problem of universality, we develop a learning algorithm which is asymptotically optimal in any Markov environment having finite memory length. Finally, we compare the performance of learning-based waveform selection to fixed rule-based waveform selection strategies for the scenarios of dynamic spectrum access and multiple-target tracking. We draw conclusions as to when learning-based approaches are expected to significantly outperform rule-based strategies, as well as the converse.en
dc.description.abstractgeneralModern radar systems must operate in a wide variety of time-varying conditions. These include various types of interference from neighboring systems, self-interference or clutter, and targets with fluctuating responses. It has been well-established that the quality and nature of radar measurements depend heavily on the choice of signal transmitted by the radar. In this dissertation, we discuss techniques which may be used to adapt the radar's waveform on-the-fly while making very few explicit assumptions about the physical environment. By employing tools from reinforcement learning and online learning, we present a variety of algorithms which handle practical and theoretical issues of the waveform selection problem that have been left open by previous works. We begin by asking the questions "What is cognitive radar?" and "When should cognitive radar be used?" in order to develop a broad mathematical framework for the signal selection problem. The latter chapters then deal with the role of intelligent real-time decision-making algorithms which select favorable signals for target tracking and interference mitigation. We conclude by discussing the possible roles of cognitive radar within future wireless networks and larger autonomous systems.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:36917en
dc.identifier.urihttp://hdl.handle.net/10919/115081en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectRadar Systemsen
dc.subjectArtificial Intelligenceen
dc.subjectSignal Processingen
dc.subjectStatistical Inferenceen
dc.titleOn the Value of Online Learning for Cognitive Radar Waveform Selectionen
dc.typeDissertationen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Thornton_CE_D_2023.pdf
Size:
8.38 MB
Format:
Adobe Portable Document Format