Multipath detection based on K-means clustering
The aim of this paper is to propose a multipath detection algorithm, which is based on K-means clustering that belongs to the class of unsupervised machine learning algorithms. The algorithm processes measurement sets computed for each satellite, namely, carrier phase, pseudorange and carrier-to-noi...
Published in: | ION GNSS+, The International Technical Meeting of the Satellite Division of The Institute of Navigation, Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019) |
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Online Access: | http://hdl.handle.net/11583/2772092 https://doi.org/10.33012/2019.17028 https://www.ion.org/publications/abstract.cfm?articleID=17028 |
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ftpoltorinoiris:oai:iris.polito.it:11583/2772092 2024-02-11T09:57:02+01:00 Multipath detection based on K-means clustering Savas C. Dovis F. Savas, C. Dovis, F. 2019 ELETTRONICO http://hdl.handle.net/11583/2772092 https://doi.org/10.33012/2019.17028 https://www.ion.org/publications/abstract.cfm?articleID=17028 eng eng Institute of Navigation country:USA place:WASHINGTON, DC 20005 info:eu-repo/semantics/altIdentifier/isbn/0-936406-23-2 info:eu-repo/semantics/altIdentifier/isbn/978-093640623-7 info:eu-repo/semantics/altIdentifier/wos/WOS:000568618903069 ispartofbook:Proceedings of the 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2019 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2019 firstpage:3801 lastpage:3811 numberofpages:11 info:eu-repo/grantAgreement/EC/H2020/corda__h2020::6e9a7dd20047847f41d755f7cb46bbeb http://hdl.handle.net/11583/2772092 doi:10.33012/2019.17028 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85075270174 https://www.ion.org/publications/abstract.cfm?articleID=17028 GNSS multipath K-mean clustering info:eu-repo/semantics/conferenceObject 2019 ftpoltorinoiris https://doi.org/10.33012/2019.17028 2024-01-16T23:14:59Z The aim of this paper is to propose a multipath detection algorithm, which is based on K-means clustering that belongs to the class of unsupervised machine learning algorithms. The algorithm processes measurement sets computed for each satellite, namely, carrier phase, pseudorange and carrier-to-noise ratio, creating clusters of consistent measurements, thus allowing the identification of satellite signals suffering from the multipath error. Since it is an unsupervised method, it overcomes one of the most limiting features of supervised algorithms that require training data sets a-priori obtained as representative of multipath and no-multipath conditions. The study exploits both the real GNSS data affected by the multipath in the surrounding environment that were collected at South African Antarctic research base SANAE-IV and the simulated data where the ionospheric, tropospheric and multipath errors are modelled. Receiver Autonomous Integrity Monitoring (RAIM) algorithm with parity method was also implemented and tested for the same datasets, and it will be used as a term of comparison for the algorithm performance. Conference Object Antarc* Antarctic PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino) Antarctic Base SANAE IV ENVELOPE(-2.850,-2.850,-71.667,-71.667) SANAE ENVELOPE(-2.850,-2.850,-71.667,-71.667) SANAE IV ENVELOPE(-2.850,-2.850,-71.667,-71.667) ION GNSS+, The International Technical Meeting of the Satellite Division of The Institute of Navigation, Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019) 3801 3811 |
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Open Polar |
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PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino) |
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English |
topic |
GNSS multipath K-mean clustering |
spellingShingle |
GNSS multipath K-mean clustering Savas C. Dovis F. Multipath detection based on K-means clustering |
topic_facet |
GNSS multipath K-mean clustering |
description |
The aim of this paper is to propose a multipath detection algorithm, which is based on K-means clustering that belongs to the class of unsupervised machine learning algorithms. The algorithm processes measurement sets computed for each satellite, namely, carrier phase, pseudorange and carrier-to-noise ratio, creating clusters of consistent measurements, thus allowing the identification of satellite signals suffering from the multipath error. Since it is an unsupervised method, it overcomes one of the most limiting features of supervised algorithms that require training data sets a-priori obtained as representative of multipath and no-multipath conditions. The study exploits both the real GNSS data affected by the multipath in the surrounding environment that were collected at South African Antarctic research base SANAE-IV and the simulated data where the ionospheric, tropospheric and multipath errors are modelled. Receiver Autonomous Integrity Monitoring (RAIM) algorithm with parity method was also implemented and tested for the same datasets, and it will be used as a term of comparison for the algorithm performance. |
author2 |
Savas, C. Dovis, F. |
format |
Conference Object |
author |
Savas C. Dovis F. |
author_facet |
Savas C. Dovis F. |
author_sort |
Savas C. |
title |
Multipath detection based on K-means clustering |
title_short |
Multipath detection based on K-means clustering |
title_full |
Multipath detection based on K-means clustering |
title_fullStr |
Multipath detection based on K-means clustering |
title_full_unstemmed |
Multipath detection based on K-means clustering |
title_sort |
multipath detection based on k-means clustering |
publisher |
Institute of Navigation |
publishDate |
2019 |
url |
http://hdl.handle.net/11583/2772092 https://doi.org/10.33012/2019.17028 https://www.ion.org/publications/abstract.cfm?articleID=17028 |
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ENVELOPE(-2.850,-2.850,-71.667,-71.667) ENVELOPE(-2.850,-2.850,-71.667,-71.667) ENVELOPE(-2.850,-2.850,-71.667,-71.667) |
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Antarctic Base SANAE IV SANAE SANAE IV |
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Antarctic Base SANAE IV SANAE SANAE IV |
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Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
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info:eu-repo/semantics/altIdentifier/isbn/0-936406-23-2 info:eu-repo/semantics/altIdentifier/isbn/978-093640623-7 info:eu-repo/semantics/altIdentifier/wos/WOS:000568618903069 ispartofbook:Proceedings of the 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2019 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2019 firstpage:3801 lastpage:3811 numberofpages:11 info:eu-repo/grantAgreement/EC/H2020/corda__h2020::6e9a7dd20047847f41d755f7cb46bbeb http://hdl.handle.net/11583/2772092 doi:10.33012/2019.17028 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85075270174 https://www.ion.org/publications/abstract.cfm?articleID=17028 |
op_doi |
https://doi.org/10.33012/2019.17028 |
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ION GNSS+, The International Technical Meeting of the Satellite Division of The Institute of Navigation, Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019) |
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