{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101095"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101095","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Plane wave parameter estimation using gps estimates of total electron content in a neural network","abstract":"Global Positioning System (GPS) signals provide us with a unique opportunity to continually monitor the free electron density in the ionosphere. Physical phenomena, such as tsunamis, have been shown to create wave features in the free electron density. The parameterization of these waves is of interest to the scientific community. Here, we investigate the application of neural networks as our parameter estimator. In this study, we provide a background on the use of GPS signals, as used to quantify the total number of free electrons between a satellite and a receiver. Following this, we provide an analysis of the neural network, starting from a basic neuron, and discuss the means by which a network is able to perform both classification and regression. We then describe in detail the methodology we use to construct a network which utilizes Doppler frequency and velocity information to estimate the waveheading, wavelength, and frequency of a plane wave. After an evaluation of our simulated environment, we apply our network to GPS data captured during the 11 March 2011 Tohoku tsunami.","abstract_html":"Global Positioning System (GPS) signals provide us with a unique opportunity to continually monitor the free electron density in the ionosphere. Physical phenomena, such as tsunamis, have been shown to create wave features in the free electron density. The parameterization of these waves is of interest to the scientific community. Here, we investigate the application of neural networks as our parameter estimator. In this study, we provide a background on the use of GPS signals, as used to quantify the total number of free electrons between a satellite and a receiver. Following this, we provide an analysis of the neural network, starting from a basic neuron, and discuss the means by which a network is able to perform both classification and regression. We then describe in detail the methodology we use to construct a network which utilizes Doppler frequency and velocity information to estimate the waveheading, wavelength, and frequency of a plane wave. After an evaluation of our simulated environment, we apply our network to GPS data captured during the 11 March 2011 Tohoku tsunami.","abstract_has_math":false,"creators":["Smith, Aaron David"],"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":2018,"date_issued":"2018-09-04T20:32:03Z","date_published":"2018-09-04T20:32:03Z","updated_at":"2026-07-22T22:24:38Z","subjects":["GPS","Global Positioning System","Neural Network","Total Electron Content","TEC","TID","Traveling Ionospheric Disturbance","Plane Wave","Estimation","Doppler","Tohoku","Pierce Point"],"languages":["en"],"rights":["Copyright 2018 Aaron Smith"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101095","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":["Smith, Aaron David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:32:03Z","2018-04-26","2018-05"]},{"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":["GPS","Global Positioning System","Neural Network","Total Electron Content","TEC","TID","Traveling Ionospheric Disturbance","Plane Wave","Estimation","Doppler","Tohoku","Pierce Point"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Aaron Smith"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101095"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Global Positioning System (GPS) signals provide us with a unique opportunity to continually monitor the free electron density in the ionosphere. Physical phenomena, such as tsunamis, have been shown to create wave features in the free electron density. The parameterization of these waves is of interest to the scientific community. Here, we investigate the application of neural networks as our parameter estimator. In this study, we provide a background on the use of GPS signals, as used to quantify the total number of free electrons between a satellite and a receiver. Following this, we provide an analysis of the neural network, starting from a basic neuron, and discuss the means by which a network is able to perform both classification and regression. We then describe in detail the methodology we use to construct a network which utilizes Doppler frequency and velocity information to estimate the waveheading, wavelength, and frequency of a plane wave. After an evaluation of our simulated environment, we apply our network to GPS data captured during the 11 March 2011 Tohoku tsunami.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Aaron Smith, accepted the attached license on 2018-04-26 at 14:32.","The student, Aaron Smith, submitted this Thesis for approval on 2018-04-26 at 14:54.","This Thesis was approved for publication on 2018-04-26 at 16:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12528 on 2018-08-31 at 17:15:12","Made available in DSpace on 2018-09-04T20:32:03Z (GMT). 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Here, we investigate the application of neural networks as our parameter estimator. In this study, we provide a background on the use of GPS signals, as used to quantify the total number of free electrons between a satellite and a receiver. Following this, we provide an analysis of the neural network, starting from a basic neuron, and discuss the means by which a network is able to perform both classification and regression. We then describe in detail the methodology we use to construct a network which utilizes Doppler frequency and velocity information to estimate the waveheading, wavelength, and frequency of a plane wave. After an evaluation of our simulated environment, we apply our network to GPS data captured during the 11 March 2011 Tohoku tsunami.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Aaron Smith, accepted the attached license on 2018-04-26 at 14:32.","The student, Aaron Smith, submitted this Thesis for approval on 2018-04-26 at 14:54.","This Thesis was approved for publication on 2018-04-26 at 16:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12528 on 2018-08-31 at 17:15:12","Made available in DSpace on 2018-09-04T20:32:03Z (GMT). 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