{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/119307"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/119307","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Enhanced Weak Signal Detection Using SVM Based Correlation Algorithm","abstract":"Traditional signal detection algorithms are often robust and are typically sufficient for high SNR data. However, the assumptions behind these methods begin to fall apart when signal period becomes either very short, or small in amplitude compared to any corruptive noise. To address this a kernel transform based cross-correlation algorithm is proposed for the application of weak signal detection. The algorithm leverages kernel methods to inflate SNR of the data and enhance the noise rejection capabilities of the traditional cross-correlation. The goal of the algorithm is to achieve detection for signals past the limits of those of the matched filter and the cross-correlation in the presence of white and colored noise. To evaluate the effectiveness of the correlation algorithm, Monte Carlo simulations are performed to determine the performance in the context of different types of noise. The performance of the algorithm will be compared against the cross-correlation and the matched filter.","abstract_html":"Traditional signal detection algorithms are often robust and are typically sufficient for high SNR data. However, the assumptions behind these methods begin to fall apart when signal period becomes either very short, or small in amplitude compared to any corruptive noise. To address this a kernel transform based cross-correlation algorithm is proposed for the application of weak signal detection. The algorithm leverages kernel methods to inflate SNR of the data and enhance the noise rejection capabilities of the traditional cross-correlation. The goal of the algorithm is to achieve detection for signals past the limits of those of the matched filter and the cross-correlation in the presence of white and colored noise. To evaluate the effectiveness of the correlation algorithm, Monte Carlo simulations are performed to determine the performance in the context of different types of noise. The performance of the algorithm will be compared against the cross-correlation and the matched filter.","abstract_has_math":false,"creators":["Kramer, Samuel Leonard"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Wicks, Alfred L."],"committee_members":["Fuller, Christopher R.","Southward, Steve C."],"year":2024,"date_issued":"2024-06-05","date_published":"2024-06-05","updated_at":"2026-07-22T22:19:01Z","subjects":["Digital Signal Processing","Signal Detection"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:40881"],"render_values":[{"text":"vt_gsexam:40881","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/119307","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Wicks, Alfred L."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Fuller, Christopher R.","Southward, Steve C."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Kramer, Samuel Leonard"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-06-06T08:01:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-06-06T08:01:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-06-05"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Digital Signal Processing","Signal Detection"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:40881"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/119307"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Traditional signal detection algorithms are often robust and are typically sufficient for high SNR data. However, the assumptions behind these methods begin to fall apart when signal period becomes either very short, or small in amplitude compared to any corruptive noise. To address this a kernel transform based cross-correlation algorithm is proposed for the application of weak signal detection. The algorithm leverages kernel methods to inflate SNR of the data and enhance the noise rejection capabilities of the traditional cross-correlation. The goal of the algorithm is to achieve detection for signals past the limits of those of the matched filter and the cross-correlation in the presence of white and colored noise. To evaluate the effectiveness of the correlation algorithm, Monte Carlo simulations are performed to determine the performance in the context of different types of noise. The performance of the algorithm will be compared against the cross-correlation and the matched filter."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["As society advances, the tools we rely on become increasingly more intricate. Pivotal to the development of these systems is the algorithms used to process the data they collect. Particularly crucial to the field of signal processing, weak signal detection is focused on the processing of barely comprehensible data in the context of powerful noise. In recent years, advancements in weak signal detection have focused on pushing the theoretical limits of signal discernibility, especially when heavily obscured by noise. Leveraging the power of machine learning, certain AI algorithms have showcased promise in the detection of weak signals. It has yet to be seen if a foundational principle of AI called a kernel transform can be applied to classic signal detection theory to increase detection performance. This thesis will propose a kernel based detection algorithm for weak signal detection and the performance of the algorithm will be compared against previously established theory. New breakthroughs in detection algorithms facilitate improvements in active and passive sonar, medical devices and even the finance sector."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Enhanced Weak Signal Detection Using SVM Based Correlation Algorithm"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Wicks, Alfred L."],"dc:contributor.committeemember":["Fuller, Christopher R.","Southward, Steve C."],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Kramer, Samuel Leonard"],"dc:date.accessioned":["2024-06-06T08:01:15Z"],"dc:date.available":["2024-06-06T08:01:15Z"],"dc:date.issued":["2024-06-05"],"dc:description.abstract":["Traditional signal detection algorithms are often robust and are typically sufficient for high SNR data. However, the assumptions behind these methods begin to fall apart when signal period becomes either very short, or small in amplitude compared to any corruptive noise. To address this a kernel transform based cross-correlation algorithm is proposed for the application of weak signal detection. The algorithm leverages kernel methods to inflate SNR of the data and enhance the noise rejection capabilities of the traditional cross-correlation. The goal of the algorithm is to achieve detection for signals past the limits of those of the matched filter and the cross-correlation in the presence of white and colored noise. To evaluate the effectiveness of the correlation algorithm, Monte Carlo simulations are performed to determine the performance in the context of different types of noise. 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This thesis will propose a kernel based detection algorithm for weak signal detection and the performance of the algorithm will be compared against previously established theory. New breakthroughs in detection algorithms facilitate improvements in active and passive sonar, medical devices and even the finance sector."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:40881"],"dc:identifier.uri":["https://hdl.handle.net/10919/119307"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Digital Signal Processing","Signal Detection"],"dc:title":["Enhanced Weak Signal Detection Using SVM Based Correlation Algorithm"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:01Z"}