The Global Positioning System (GPS) and other satellite-based positioning systems are often a key component in applications requiring localization. However, accurate positioning in areas with poor GPS coverage, such as inside buildings and in dense cities, is in increasing demand for many modern applications. Fortunately, recent developments in ultra-wideband (UWB) radio technology have enabled precise positioning in places where it was not previously possible by utilizing multipath-resistant wide band pulses.
Although ultra-wideband signals are less prone to multipath interference, it is still a bottleneck as increasingly ambitious projects continue to demand higher precision. Some UWB radios include on-board detection of multipath conditions, however the implementations are usually limited to basic condition checks. In order to address these shortcomings, We propose an application of machine learning to reliably detect non-line-of-sight conditions when the on-board radio classifier fails to recognize these conditions. Our solution includes a neural network classifier that is 99.98% accurate in a variety of environments.
","abstract_html":"<p>The Global Positioning System (GPS) and other satellite-based positioning systems are often a key component in applications requiring localization. However, accurate positioning in areas with poor GPS coverage, such as inside buildings and in dense cities, is in increasing demand for many modern applications. Fortunately, recent developments in ultra-wideband (UWB) radio technology have enabled precise positioning in places where it was not previously possible by utilizing multipath-resistant wide band pulses.</p> <p>Although ultra-wideband signals are less prone to multipath interference, it is still a bottleneck as increasingly ambitious projects continue to demand higher precision. Some UWB radios include on-board detection of multipath conditions, however the implementations are usually limited to basic condition checks. In order to address these shortcomings, We propose an application of machine learning to reliably detect non-line-of-sight conditions when the on-board radio classifier fails to recognize these conditions. Our solution includes a neural network classifier that is 99.98% accurate in a variety of environments.</p>","abstract_has_math":false,"creators":["Mayer, Cory A"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Bruce Debruhl"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-12-01T08:00:00Z","date_published":"2018-12-01T08:00:00Z","updated_at":"2026-07-24T01:32:08Z","subjects":["ultra-wideband","machine learning","neural networks","SVM","time domain","Artificial Intelligence and Robotics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2018.139"],"render_values":[{"text":"10.15368/theses.2018.139","href":"https://doi.org/10.15368/theses.2018.139","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1957","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bruce Debruhl"]},{"key":"dc:creator","label":"Author","values":["Mayer, Cory A"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2021-12-11T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ultra-wideband","machine learning","neural networks","SVM","time domain","Artificial Intelligence and Robotics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1957","10.15368/theses.2018.139"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The Global Positioning System (GPS) and other satellite-based positioning systems are often a key component in applications requiring localization. However, accurate positioning in areas with poor GPS coverage, such as inside buildings and in dense cities, is in increasing demand for many modern applications. Fortunately, recent developments in ultra-wideband (UWB) radio technology have enabled precise positioning in places where it was not previously possible by utilizing multipath-resistant wide band pulses.
Although ultra-wideband signals are less prone to multipath interference, it is still a bottleneck as increasingly ambitious projects continue to demand higher precision. Some UWB radios include on-board detection of multipath conditions, however the implementations are usually limited to basic condition checks. In order to address these shortcomings, We propose an application of machine learning to reliably detect non-line-of-sight conditions when the on-board radio classifier fails to recognize these conditions. Our solution includes a neural network classifier that is 99.98% accurate in a variety of environments.
"]},{"key":"dc:title","label":"Title","values":["Improving Ultra-Wideband Localization by Detecting Radio Misclassification"]}]}],"canonical_facts":{"dc:contributor":["Bruce Debruhl"],"dc:creator":["Mayer, Cory A"],"dc:date.available":["2021-12-11T08:00:00Z"],"dc:description.abstract":["The Global Positioning System (GPS) and other satellite-based positioning systems are often a key component in applications requiring localization. However, accurate positioning in areas with poor GPS coverage, such as inside buildings and in dense cities, is in increasing demand for many modern applications. Fortunately, recent developments in ultra-wideband (UWB) radio technology have enabled precise positioning in places where it was not previously possible by utilizing multipath-resistant wide band pulses.
Although ultra-wideband signals are less prone to multipath interference, it is still a bottleneck as increasingly ambitious projects continue to demand higher precision. Some UWB radios include on-board detection of multipath conditions, however the implementations are usually limited to basic condition checks. In order to address these shortcomings, We propose an application of machine learning to reliably detect non-line-of-sight conditions when the on-board radio classifier fails to recognize these conditions. Our solution includes a neural network classifier that is 99.98% accurate in a variety of environments.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1957","10.15368/theses.2018.139"],"dc:subject":["ultra-wideband","machine learning","neural networks","SVM","time domain","Artificial Intelligence and Robotics"],"dc:title":["Improving Ultra-Wideband Localization by Detecting Radio Misclassification"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:32:08Z"}