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Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems
dc.contributor.author | Barral Vales, Valentín | |
dc.contributor.author | Escudero, Carlos J. | |
dc.contributor.author | García-Naya, José A. | |
dc.contributor.author | Suárez-Casal, Pedro | |
dc.date.accessioned | 2020-01-14T18:28:46Z | |
dc.date.available | 2020-01-14T18:28:46Z | |
dc.date.issued | 2019-12-10 | |
dc.identifier.citation | Barral, V.; Escudero, C.J.; García-Naya, J.A.; Suárez-Casal, P. Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems. Sensors 2019, 19, 5438. | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/2183/24632 | |
dc.description.abstract | [Abstract] Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2016-045 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-R | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | M D P I AG | es_ES |
dc.relation.uri | https://doi.org/10.3390/s19245438 | es_ES |
dc.rights | Atribución 4.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/es/ | * |
dc.subject | UWB | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Neural networks | es_ES |
dc.subject | NLOS detection | es_ES |
dc.subject | Indoor location algorithms | es_ES |
dc.title | Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Sensors | es_ES |
UDC.volume | 19 | es_ES |
UDC.issue | 24 | es_ES |
UDC.startPage | 5438 | es_ES |
dc.identifier.doi | 10.3390/s19245438 |
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