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dc.contributor.authorKlus, Roman
dc.contributor.authorKlus, Lucie
dc.contributor.authorTalvitie, Jukka
dc.contributor.authorPihlajasalo, Jaakko
dc.contributor.authorTorres-Sospedra, Joaquín
dc.contributor.authorValkama, Mikko
dc.date.accessioned2022-04-13T07:53:58Z
dc.date.available2022-04-13T07:53:58Z
dc.date.issued2021-11-29
dc.identifier.citationKLUS, Roman, et al. Transfer Learning for Convolutional Indoor Positioning Systems. En 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2022. p. 1-8.ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/197333
dc.descriptionPonencia presentada en la 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 29 Nov.-2 Dec. 2021, Lloret de Mar (Spain)ca_CA
dc.description.abstractFingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions.ca_CA
dc.format.extent8 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherIEEEca_CA
dc.relation.isPartOf2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)ca_CA
dc.rights© Copyright 2022 IEEE - All rights reserved.ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/ca_CA
dc.subjectartificial neural networkca_CA
dc.subjectWLANca_CA
dc.subjectconvolutional neural networkca_CA
dc.subjectdeep learningca_CA
dc.subjectfingerprintingca_CA
dc.subjectindoor positioningca_CA
dc.subjectmachine learningca_CA
dc.subjecttransfer learningca_CA
dc.titleTransfer Learning for Convolutional Indoor Positioning Systemsca_CA
dc.typeinfo:eu-repo/semantics/conferenceObjectca_CA
dc.identifier.doi10.1109/IPIN51156.2021.9662544
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.type.versioninfo:eu-repo/semantics/submittedVersionca_CA


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