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Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning
dc.contributor.author | Torres-Sospedra, Joaquín | |
dc.contributor.author | Aranda Polo, Fernando Jesús | |
dc.contributor.author | Álvarez, Fernando J. | |
dc.contributor.author | Quezada Gaibor, Darwin | |
dc.contributor.author | Silva, Ivo | |
dc.contributor.author | Pendão, Cristiano | |
dc.contributor.author | Moreira, Adriano | |
dc.date.accessioned | 2021-11-23T08:50:40Z | |
dc.date.available | 2021-11-23T08:50:40Z | |
dc.date.issued | 2021-06-15 | |
dc.identifier.citation | J. Torres-Sospedra et al., "Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning," 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1-5, doi: 10.1109/VTC2021-Spring51267.2021.9448947. | ca_CA |
dc.identifier.isbn | 978-1-7281-8964-2 | |
dc.identifier.uri | http://hdl.handle.net/10234/195616 | |
dc.description | Ponencia presentada en 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 25-28 April 2021 | ca_CA |
dc.description.abstract | Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average | ca_CA |
dc.format.extent | 5 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Institute of Electrical and Electronics Engineers | ca_CA |
dc.publisher | IEEE | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | indoor positioning | ca_CA |
dc.subject | fingerprinting | ca_CA |
dc.subject | radio map | ca_CA |
dc.subject | noisy samples | ca_CA |
dc.subject | ensemble | ca_CA |
dc.subject | vehicular and wireless technologies | ca_CA |
dc.subject | radio navigation | ca_CA |
dc.subject | estimation | ca_CA |
dc.subject | machine learning | ca_CA |
dc.subject | fingerprint recognition | ca_CA |
dc.subject | noise generators | ca_CA |
dc.subject | noise measurement | ca_CA |
dc.title | Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning | ca_CA |
dc.type | info:eu-repo/semantics/conferenceObject | ca_CA |
dc.identifier.doi | https://doi.org/10.1109/VTC2021-Spring51267.2021.9448947 | |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/813278 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://ieeexplore.ieee.org/xpl/conhome/9448628/proceeding | ca_CA |
dc.type.version | info:eu-repo/semantics/submittedVersion | ca_CA |
project.funder.name | Ministerio de Ciencia, Innovación y Universidades (Spain) | ca_CA |
project.funder.name | Ministerio de Economía y Competitividad | ca_CA |
project.funder.name | European Union’s Horizon 2020 Research | ca_CA |
project.funder.name | Fundaçao para a Ciência e Tecnologia | ca_CA |
oaire.awardNumber | PTQ2018-009981 | ca_CA |
oaire.awardNumber | RTI2018-095168-B-C54 | ca_CA |
oaire.awardNumber | TEC2017-90808-REDT | ca_CA |
oaire.awardNumber | UIDB/00319/2020 | ca_CA |
oaire.awardNumber | PD/BD/137401/2018 | ca_CA |