Publication:

Using Machine Learning to Localize BLE devices on a Single Anchor

Date

2025
Proceedings Paper
https://doi.org/10.1109/eucnc/6gsummit63408.2025.11037133
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cris.virtual.orcid0000-0001-5942-9440
cris.virtual.orcid0000-0001-8900-4881
cris.virtual.orcid0000-0003-1943-6261
cris.virtual.orcid0000-0002-0214-5751
cris.virtualsource.department78e08ab8-aadb-4883-92c9-643e40198fef
cris.virtualsource.departmentbe6d7d02-2026-441e-a691-0f83be710a9a
cris.virtualsource.department775007c5-854e-4f51-9a21-92e054f36393
cris.virtualsource.departmenteb7ed649-7114-4ead-84d3-05a804e8fb45
cris.virtualsource.orcid78e08ab8-aadb-4883-92c9-643e40198fef
cris.virtualsource.orcidbe6d7d02-2026-441e-a691-0f83be710a9a
cris.virtualsource.orcid775007c5-854e-4f51-9a21-92e054f36393
cris.virtualsource.orcideb7ed649-7114-4ead-84d3-05a804e8fb45
dc.contributor.authorLeitch, Samuel G.
dc.contributor.authorAhmed, Qasim Zeeshan
dc.contributor.authorFontaine, Jaron
dc.contributor.authorVan Herbruggen, Ben
dc.contributor.authorShahid, Adnan
dc.contributor.authorDe Poorter, Eli
dc.contributor.authorLazaridis, Pavlos
dc.date.accessioned2026-04-13T14:24:28Z
dc.date.available2026-04-13T14:24:28Z
dc.date.createdwos2025-11-01
dc.date.issued2025
dc.description.abstractIndoor localization using Bluetooth Low Energy (BLE) technology can be accomplished by a variety of methods. One appreciable benefits is the single-anchor solution, which allows for low-cost deployments. In this paper, five different methods of single-anchor localization have been investigated, including different methods of determining the angle of arrival and distance estimation. The best performing single-anchor localization method was found to be a dedicated machine learning algorithm whose output is the location of the target device. Once a Kalman filter was applied to it, it achieved a mean distance error of 0.34 m on the test scenario.
dc.description.wosFundingTextThis work is supported in part by the EPSRC DTP, EPSRC U.K., and in part by EVOLVE-MSCA-Research and Innovation Staff Exchange (SE)under RC Grant EP/X039765/1 and Grant ID: 101086218.
dc.identifier.doi10.1109/eucnc/6gsummit63408.2025.11037133
dc.identifier.isbn979-8-3503-9181-7
dc.identifier.issn2475-6490
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/59068
dc.language.isoeng
dc.provenance.editstepusergreet.vanhoof@imec.be
dc.publisherIEEE
dc.source.beginpage229
dc.source.conference2025 Joint European Conference on Networks and Communications & 6G Summit, EuCNC/6G Summit
dc.source.conferencedate2025
dc.source.conferencelocationPoznan, Poland
dc.source.endpage234
dc.source.journal2025 Joint European Conference on Networks and Communications & 6G Summit, EuCNC/6G Summit
dc.source.numberofpages6
dc.subject.keywordsINDOOR
dc.subject.keywordsSYSTEM
dc.subject.keywordsPHASE
dc.title

Using Machine Learning to Localize BLE devices on a Single Anchor

dc.typeProceedings paper
dspace.entity.typePublication
imec.internal.crawledAt2026-04-07
imec.internal.sourcecrawler
imec.internal.wosCreatedAt2026-04-07
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