{"id":{"repo_id":"ku","oai_identifier":"oai:kuscholarworks.ku.edu:1808/37660"},"canonical_url":"https://search.dev.ndltd.org/etd/ku/oai:kuscholarworks.ku.edu:1808/37660","repository":{"repo_id":"ku","name":"University of Kansas","base_url":"https://kuscholarworks.ku.edu/server/oai/request"},"display":{"title":"Source Separation using Sparse Bayesian Learning","abstract":"Wireless communication in recent decades has allowed for a substantial increase in both the speed and capacity of information which may be transmitted over large distances. However, given the expanding societal needs coupled with a finite available spectrum, the question arises of how to increase the efficiency by which information may be transmitted. One natural answer to this question lies in spectrum sharing—that is, in allowing multiple noncooperative agents to inhabit the same spectrum bands. In order to achieve this, we must be able to reliably separate the desired signals from those of other agents in the background. However, since our agents are noncooperative, we must develop a model-agnostic approach at tackling this problem. For this work, we will consider cohabitation between radar signals and communication signals, with the former being the desired signal and the latter being the noncooperative agent. In order to approach such problems involving highly underdetermined linear systems, we propose utilizing Sparse Bayesian Learning and present our results on selected problems.","abstract_html":"Wireless communication in recent decades has allowed for a substantial increase in both the speed and capacity of information which may be transmitted over large distances. However, given the expanding societal needs coupled with a finite available spectrum, the question arises of how to increase the efficiency by which information may be transmitted. One natural answer to this question lies in spectrum sharing—that is, in allowing multiple noncooperative agents to inhabit the same spectrum bands. In order to achieve this, we must be able to reliably separate the desired signals from those of other agents in the background. However, since our agents are noncooperative, we must develop a model-agnostic approach at tackling this problem. For this work, we will consider cohabitation between radar signals and communication signals, with the former being the desired signal and the latter being the noncooperative agent. In order to approach such problems involving highly underdetermined linear systems, we propose utilizing Sparse Bayesian Learning and present our results on selected problems.","abstract_has_math":false,"creators":["El-Katri, Faris Ahmed"],"institution":"University of Kansas","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["McCormick, Patrick M."],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-31","date_published":"2025-05-31","updated_at":"2026-07-24T02:46:52Z","subjects":["Electrical engineering","Deconvolution","Radar Signal Processing","Sparse Bayesian Learning"],"languages":["en"],"rights":["This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["https://www.proquest.com/LegacyDocView/DISSNUM/31939996"],"render_values":[{"text":"https://www.proquest.com/LegacyDocView/DISSNUM/31939996","href":"https://www.proquest.com/LegacyDocView/DISSNUM/31939996","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1808/37660","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["McCormick, Patrick M."]},{"key":"dc:creator","label":"Author","values":["El-Katri, Faris Ahmed"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-04-15T21:37:06Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-04-15T21:37:06Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05-31"]},{"key":"dc:publisher","label":"Institution","values":["University of Kansas"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical engineering","Deconvolution","Radar Signal Processing","Sparse Bayesian Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["https://www.proquest.com/LegacyDocView/DISSNUM/31939996"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1808/37660"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Wireless communication in recent decades has allowed for a substantial increase in both the speed and capacity of information which may be transmitted over large distances. 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One natural answer to this question lies in spectrum sharing—that is, in allowing multiple noncooperative agents to inhabit the same spectrum bands. In order to achieve this, we must be able to reliably separate the desired signals from those of other agents in the background. However, since our agents are noncooperative, we must develop a model-agnostic approach at tackling this problem. For this work, we will consider cohabitation between radar signals and communication signals, with the former being the desired signal and the latter being the noncooperative agent. 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