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MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning

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dc.contributor.authorCastillo-Cara, Manuel
dc.contributor.authorMartínez-Gómez, Jesús
dc.contributor.authorBallesteros-Jerez, Javier
dc.contributor.authorGarcía-Varea, Ismael
dc.contributor.authorGarcía-Castro, Raúl
dc.contributor.authorOrozco-Barbosa, Luis
dc.date.accessioned2025-10-14T14:48:53Z
dc.date.available2025-10-14T14:48:53Z
dc.date.issued2025-03-31
dc.descriptionThe registered version of this article, first published in IEEE Journal of Selected Topics in Signal Processing, is available online at the publisher's website: Institute of Electrical and Electronics Engineers (IEEE), https://doi.org/10.1109/JSTSP.2025.3555067
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en IEEE Journal of Selected Topics in Signal Processing, está disponible en línea en el sitio web del editor: Institute of Electrical and Electronics Engineers (IEEE), https://doi.org/10.1109/JSTSP.2025.3555067
dc.descriptionThis work was supported in part by the Government of Castilla-La Mancha and “ERDF A way of making Europe” under Project SBPLY/21/180225/000062, in part by CYTED under Grant 520rt0011, in part by the Spanish Government (AEI) under Projects PID2022-137344OB-C33 /AEI/10.13039/501100011033, Project PID2021-123627OB-C52, and Project PID2019–106758GB–C33, in part by Madrid Government (Comunidad de Madrid-Spain) through the Multiannual Agreement with the Universidad Politécnica de Madrid in the Excellence Programme for University Teaching Staff, in the context of the V PRICIT (Regional Programme of Research and TechnologicalInnovation),andinpartbytheUniversidaddeCastilla-LaMancha and “ERDF A way of making Europe” under Project 2022-GRIN-34437.
dc.description.abstractIndoor localization determines an object’s position within enclosed spaces, with applications in navigation, asset tracking, robotics, and context-aware computing. Technologies range from WiFi and Bluetooth to advanced systems like Massive Multiple Input-Multiple Output (MIMO). MIMO, initially designed to enhance wireless communication, is now key in indoor positioning due to its spatial diversity and multipath propagation. This study integrates MIMO-based indoor localization with Hybrid Neural Networks (HyNN), converting structured datasets into synthetic images using TINTO. This research marks the first application of HyNNs using synthetic images for MIMO-based indoor localization. Our key contributions include: (i) adapting TINTO for regression problems; (ii) using synthetic images as input data for our model; (iii) designing a novel HyNN with a Convolutional Neural Network branch for synthetic images and an MultiLayer Percetron branch for tidy data; and (iv) demonstrating improved results and metrics compared to prior literature. These advancements highlight the potential of HyNNs in enhancing the accuracy and efficiency of indoor localization systems.en
dc.description.versionversión publicada
dc.identifier.citationM. Castillo-Cara, J. Martínez-Gómez, J. Ballesteros-Jerez, I. García-Varea, R. García-Castro and L. Orozco-Barbosa, "MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning," in IEEE Journal of Selected Topics in Signal Processing, vol. 19, no. 3, pp. 559-571, April 2025
dc.identifier.doihttps://doi.org/10.1109/JSTSP.2025.3555067
dc.identifier.issn1941-0484
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30409
dc.journal.issue3
dc.journal.titleIEEE Journal of Selected Topics in Signal Processing
dc.journal.volume19
dc.language.isoen
dc.page.final571
dc.page.initial559
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject1203.04 Inteligencia artificial
dc.subject.keywordsmassive MIMOen
dc.subject.keywordsdeep learningen
dc.subject.keywordshybrid neural networken
dc.subject.keywordssynthetic imagesen
dc.subject.keywordspositioningen
dc.subject.keywordsindoor localisationes
dc.titleMIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learningen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationc0e39bd2-c0d8-4743-953d-488baf6b977e
relation.isAuthorOfPublication.latestForDiscoveryc0e39bd2-c0d8-4743-953d-488baf6b977e
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