Identificador persistente para citar o vincular este elemento: https://accedacris.ulpgc.es/handle/10553/134968
Campo DC Valoridioma
dc.contributor.authorNikodem, MacIej-
dc.contributor.authorTrajnowicz, Grzegorz-
dc.contributor.authorDe Blasio , Gabriele Salvatore-
dc.contributor.authorQuesada Arencibia, Francisco Alexis-
dc.date.accessioned2024-12-11T10:08:27Z-
dc.date.available2024-12-11T10:08:27Z-
dc.date.issued2024-
dc.identifier.issn1530-437X-
dc.identifier.otherScopus-
dc.identifier.urihttps://accedacris.ulpgc.es/handle/10553/134968-
dc.description.abstractHistorically, the first indoor localization method using Bluetooth Low Energy (BLE) relied on received signal strength indicator (RSSI) measurements. This simple and cost-effective method suffers from limited accuracy due to its high susceptibility to environmental factors. The introduction of angle-of-arrival (AoA) functionality in BLE v5.1 significantly improved accuracy by enabling signal phase measurements. AoA exploits the phase difference of the received signal across spatially distributed antennas to estimate the direction of arrival and then pinpoint the location using triangulation. In contrast, multi-carrier phase difference (MCPD) leverages the phase difference of the signal at a single antenna but across different frequencies. The phase difference is used to estimate the distance, which allows the location to be determined using multilateration. MCPD is a recent addition to the BLE standard known as channel sounding, but unfortunately, real-world evaluations of this method are scarce. This paper addresses this gap by experimentally analyzing the performance of MCPD in an indoor environment. We conduct comprehensive experiments in a 100m2 office space, comparing the accuracy of MCPD with RSSI and AoA-based approaches in the same area. The results show that the MCPD technique yields similar localization accuracy to the AoA method, with a mean localization error of 0.98m and 1.26 m, respectively, while avoiding the need for complex antenna arrays. Additionally, MCPD significantly outperforms RSSI-based methods, which have a mean localization error of 3.83 m.-
dc.languageeng-
dc.relation.ispartofIEEE Sensors Journal-
dc.sourceIEEE Sensors Journal[ISSN 1530-437X], (Enero 2024)-
dc.subject3307 Tecnología electrónica-
dc.subject.otherAngle Of Arrival-
dc.subject.otherBluetooth Low Energy-
dc.subject.otherChannel Sounding-
dc.subject.otherIndoor Positioning-
dc.subject.otherIot-
dc.subject.otherMulti-Carrier Phase Difference-
dc.subject.otherRadio Communication-
dc.subject.otherReceived Signal Strength Indicator-
dc.titleExperimental Evaluation of Multi-Carrier Phase Difference Localization in Bluetooth Low Energy-
dc.typeinfo:eu-repo/semantics/Article-
dc.typeArticle-
dc.identifier.doi10.1109/JSEN.2024.3495030-
dc.identifier.scopus85209752302-
dc.identifier.isi001389570600022-
dc.contributor.orcid0000-0002-9242-2029-
dc.contributor.orcidNO DATA-
dc.contributor.orcid0000-0002-6233-567X-
dc.contributor.orcid0000-0002-8313-5124-
dc.contributor.authorscopusid57220342922-
dc.contributor.authorscopusid59418712800-
dc.contributor.authorscopusid59418333300-
dc.contributor.authorscopusid59418072200-
dc.identifier.eissn1558-1748-
dc.description.lastpage1560-
dc.identifier.issue1-
dc.description.firstpage1548-
dc.relation.volume25-
dc.investigacionIngeniería y Arquitectura-
dc.type2Artículo-
dc.contributor.daisngid2008602-
dc.contributor.daisngid67618084-
dc.contributor.daisngid34540717-
dc.contributor.daisngid67572333-
dc.description.numberofpages13-
dc.utils.revision-
dc.contributor.wosstandardWOS:Nikodem, M-
dc.contributor.wosstandardWOS:Trajnowicz, G-
dc.contributor.wosstandardWOS:de Blasio, GS-
dc.contributor.wosstandardWOS:Quesada-Arencibia, FA-
dc.date.coverdateEnero 2024-
dc.identifier.ulpgc-
dc.contributor.buulpgcBU-INF-
dc.description.sjr1,084-
dc.description.jcr4,3-
dc.description.sjrqQ1-
dc.description.jcrqQ1-
dc.description.scieSCIE-
dc.description.miaricds10,8-
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-6233-567X-
crisitem.author.orcid0000-0002-8313-5124-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameDe Blasio, Gabriele Salvatore-
crisitem.author.fullNameQuesada Arencibia, Francisco Alexis-
Colección:Artículos
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