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dc.contributor.authorli, Y.
dc.contributor.authorMazuelas, S. 
dc.contributor.authorShen, Y.
dc.date.accessioned2022-04-13T17:32:27Z
dc.date.available2022-04-13T17:32:27Z
dc.date.issued2022-01-24
dc.identifier.isbn978-1-6654-2390-8
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1463
dc.description.abstractRadio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative for highly accurate localization that gives all probable range values rather than a single estimate of distance. We propose a deep learning approach to generate accurate SRI from RF measurements. In particular, the proposed approach is implemented by a network with two neural modules and conducts the generation directly from raw data. Extensive experiments on a case study with two public datasets are conducted to quantify the efficiency in different indoor localization tasks. The results show that the proposed approach can generate highly accurate SRI, and significantly outperforms conventional techniques in both nonline-of-sight (NLOS) detection and ranging error mitigation.en_US
dc.description.sponsorshipRamon y Cajal Grant RYC-2016-19383en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectIndoor localization, soft range information, deep learning, ranging error mitigation, NLOS detectionen_US
dc.titleA Deep Learning Approach for Generating Soft Range Information from RF Dataen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.identifier.doi10.1109/GCWkshps52748.2021.9681832en_US
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9681832en_US
dc.relation.projectIDES/2PE/PID2019-105058GA-I00en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.title2021 IEEE Globecom Workshops (GC Wkshps)en_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España