Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems

Descripción del Articulo

Poster presented at the 2021 CEDAR Virtual Workshop, June 20-25.
Detalles Bibliográficos
Autores: Fajardo, G., Pacheco, Edgardo E.
Formato: objeto de conferencia
Fecha de Publicación:2021
Institución:Instituto Geofísico del Perú
Repositorio:IGP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.igp.gob.pe:20.500.12816/4963
Enlace del recurso:http://hdl.handle.net/20.500.12816/4963
Nivel de acceso:acceso abierto
Materia:GNSS
Machine learning
Space weather
https://purl.org/pe-repo/ocde/ford#1.05.01
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dc.title.es_ES.fl_str_mv Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems
title Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems
spellingShingle Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems
Fajardo, G.
GNSS
Machine learning
Space weather
https://purl.org/pe-repo/ocde/ford#1.05.01
title_short Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems
title_full Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems
title_fullStr Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems
title_full_unstemmed Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems
title_sort Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems
author Fajardo, G.
author_facet Fajardo, G.
Pacheco, Edgardo E.
author_role author
author2 Pacheco, Edgardo E.
author2_role author
dc.contributor.author.fl_str_mv Fajardo, G.
Pacheco, Edgardo E.
dc.subject.es_ES.fl_str_mv GNSS
Machine learning
Space weather
topic GNSS
Machine learning
Space weather
https://purl.org/pe-repo/ocde/ford#1.05.01
dc.subject.ocde.es_ES.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.05.01
description Poster presented at the 2021 CEDAR Virtual Workshop, June 20-25.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-07-09T13:11:50Z
dc.date.available.none.fl_str_mv 2021-07-09T13:11:50Z
dc.date.issued.fl_str_mv 2021-06
dc.type.es_ES.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12816/4963
url http://hdl.handle.net/20.500.12816/4963
dc.language.iso.es_ES.fl_str_mv eng
language eng
dc.rights.es_ES.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.uri.es_ES.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.es_ES.fl_str_mv application/pdf
dc.publisher.es_ES.fl_str_mv Instituto Geofísico del Perú
dc.source.none.fl_str_mv reponame:IGP-Institucional
instname:Instituto Geofísico del Perú
instacron:IGP
instname_str Instituto Geofísico del Perú
instacron_str IGP
institution IGP
reponame_str IGP-Institucional
collection IGP-Institucional
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spelling Fajardo, G.Pacheco, Edgardo E.2021-07-09T13:11:50Z2021-07-09T13:11:50Z2021-06http://hdl.handle.net/20.500.12816/4963Poster presented at the 2021 CEDAR Virtual Workshop, June 20-25.The equatorial ionosphere has been extensively studied using purely physical models, however in recent years, with a large amount of data, it has been possible to improve these models using machine learning techniques. In this paper, we share the research results aimed to evaluate the influence of space weather parameters on GPS position approximation. We evaluated data from the Huancayo GPS station between 2016 and 2020 and we have taken into account the space weather data from the OMNI website, scintillation index (S4) and position data obtained from the GPS of the LISN network to perform our model. In addition, we use tropospheric conditions provided by the Geophysical Institute of Peru (IGP). The final result is a reliability matrix obtained with an XG Boost algorithm that will allow us to evaluate if a GPS signal given the conditions is indeed reliable or not.application/pdfengInstituto Geofísico del Perúinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/GNSSMachine learningSpace weatherhttps://purl.org/pe-repo/ocde/ford#1.05.01Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systemsinfo:eu-repo/semantics/conferenceObjectreponame:IGP-Institucionalinstname:Instituto Geofísico del Perúinstacron:IGPORIGINALPoster_Fajardo_&_Pacheco_2021.pdfPoster_Fajardo_&_Pacheco_2021.pdfapplication/pdf633447https://repositorio.igp.gob.pe/bitstreams/68964dc1-901c-446d-a9dd-25701d91b38d/downloadd2efb9dde2d00064f113ec3e4d1ce07cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.igp.gob.pe/bitstreams/1a7513b3-c180-4c59-98a4-1602f03295fb/download8a4605be74aa9ea9d79846c1fba20a33MD52TEXTPoster_Fajardo_&_Pacheco_2021.pdf.txtPoster_Fajardo_&_Pacheco_2021.pdf.txtExtracted texttext/plain6093https://repositorio.igp.gob.pe/bitstreams/fb771251-0886-4640-b99e-32970e72f831/downloadd4b7b5f776a81f77a9a9d550f035e084MD53THUMBNAILPoster_Fajardo_&_Pacheco_2021.pdf.jpgPoster_Fajardo_&_Pacheco_2021.pdf.jpgIM Thumbnailimage/jpeg132294https://repositorio.igp.gob.pe/bitstreams/bee400e2-74b2-4056-804b-2d9e18bda6d1/downloadd256d760ca8846d14e93d8d5c24ee7bfMD5420.500.12816/4963oai:repositorio.igp.gob.pe:20.500.12816/49632021-07-09 14:39:55.129https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessopen.accesshttps://repositorio.igp.gob.peRepositorio Geofísico del Perudspace-help@myu.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Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems
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