{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/44090"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/44090","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Ionospheric imaging with compressed sensing","abstract":"Compressed sensing is a novel theory of sampling and reconstruction that has emerged in the past several years. It seeks to leverage the inherent sparsity of natural images to reduce the number of necessary measurements to a sub-Nyquist level. We discuss how ideas from compressed sensing can benefit ionospheric imaging in two ways. Compressed sensing suggests signal reconstruction techniques that take advantage of sparsity, offering us new ways of interpreting data, especially for undersampled problems. One example is radar imaging. We explain how compressed sensing can be used for radar imaging and show results that suggest improved performance over existing techniques. In addition to benefitting the way we use data, compressed sensing can improve how we gather data, allowing us to shift complexity from sensing to reconstruction. One example is airglow imaging, wherein we propose replacing CCD-based imagers with single-pixel, compressive imagers. This will reduce the cost of airglow imagers and allow access to spatial information at infrared wavelengths. We show preliminary simulation results suggesting this technique may be feasible for airglow imaging.","abstract_html":"Compressed sensing is a novel theory of sampling and reconstruction that has emerged in the past several years. It seeks to leverage the inherent sparsity of natural images to reduce the number of necessary measurements to a sub-Nyquist level. We discuss how ideas from compressed sensing can benefit ionospheric imaging in two ways. Compressed sensing suggests signal reconstruction techniques that take advantage of sparsity, offering us new ways of interpreting data, especially for undersampled problems. One example is radar imaging. We explain how compressed sensing can be used for radar imaging and show results that suggest improved performance over existing techniques. In addition to benefitting the way we use data, compressed sensing can improve how we gather data, allowing us to shift complexity from sensing to reconstruction. One example is airglow imaging, wherein we propose replacing CCD-based imagers with single-pixel, compressive imagers. This will reduce the cost of airglow imagers and allow access to spatial information at infrared wavelengths. We show preliminary simulation results suggesting this technique may be feasible for airglow imaging.","abstract_has_math":false,"creators":["Harding, Brian"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Makela, Jonathan J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-24T21:50:22Z","date_published":"2013-05-24T21:50:22Z","updated_at":"2026-07-22T22:25:33Z","subjects":["ionospheric imaging","radar imaging","compressed sensing","inverse methods","ionospheric irregularities","compressive imaging"],"languages":["en"],"rights":["Copyright 2013 Brian Harding"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/44090","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Makela, Jonathan J."]},{"key":"dc:creator","label":"Author","values":["Harding, Brian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-05-24T21:50:22Z","2013-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ionospheric imaging","radar imaging","compressed sensing","inverse methods","ionospheric irregularities","compressive imaging"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Brian Harding"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/44090"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Compressed sensing is a novel theory of sampling and reconstruction that has emerged in the past several years. 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We show preliminary simulation results suggesting this technique may be feasible for airglow imaging.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-04-16T22:13:17Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Harding_Brian.pdf: 1494673 bytes, checksum: cd6c3228f9c37e3ebfd64313c5d25b06 (MD5)","Made available in DSpace on 2013-05-24T21:50:22Z (GMT). No. of bitstreams: 2 Brian_Harding.pdf: 1494673 bytes, checksum: cd6c3228f9c37e3ebfd64313c5d25b06 (MD5) license.txt: 4063 bytes, checksum: c2decd32a62a5786679cec1a16da5ae7 (MD5)"]},{"key":"dc:title","label":"Title","values":["Ionospheric imaging with compressed sensing"]}]}],"canonical_facts":{"dc:contributor":["Makela, Jonathan J."],"dc:creator":["Harding, Brian"],"dc:date":["2013-05-24T21:50:22Z","2013-05"],"dc:description":["Compressed sensing is a novel theory of sampling and reconstruction that has emerged in the past several years. It seeks to leverage the inherent sparsity of natural images to reduce the number of necessary measurements to a sub-Nyquist level. We discuss how ideas from compressed sensing can benefit ionospheric imaging in two ways. Compressed sensing suggests signal reconstruction techniques that take advantage of sparsity, offering us new ways of interpreting data, especially for undersampled problems. One example is radar imaging. We explain how compressed sensing can be used for radar imaging and show results that suggest improved performance over existing techniques. In addition to benefitting the way we use data, compressed sensing can improve how we gather data, allowing us to shift complexity from sensing to reconstruction. One example is airglow imaging, wherein we propose replacing CCD-based imagers with single-pixel, compressive imagers. This will reduce the cost of airglow imagers and allow access to spatial information at infrared wavelengths. 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