Using Machine Learning to Localize BLE Devices on a Single Anchor

Samuel Leitch; Qasim Ahmed; Jaron Fontaine; Ben Van Herbruggen; Adnan Shahid; Eli De Poorter; Pavlos Lazaridis

doi:10.1109/EuCNC/6GSummit63408.2025.11037133

Using Machine Learning to Localize BLE Devices on a Single Anchor

Samuel Leitch, Qasim Ahmed, Jaron Fontaine, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter, Pavlos Lazaridis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Indoor 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.

Original language	English
Title of host publication	2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	229-234
Number of pages	6
ISBN (Electronic)	9798350391800
ISBN (Print)	9798350391817
DOIs	https://doi.org/10.1109/EuCNC/6GSummit63408.2025.11037133
Publication status	Published - 26 Jun 2025
Event	2025 Joint European Conference on Networks and Communications & 6G Summit - Poznan, Poland Duration: 3 Jun 2025 → 6 Jun 2025 https://www.eucnc.eu/

Publication series

Name	European Conference on Networks and Communications
Publisher	IEEE
ISSN (Print)	2475-6490
ISSN (Electronic)	2575-4912

Conference

Conference	2025 Joint European Conference on Networks and Communications & 6G Summit
Abbreviated title	EuCNC/6G Summit
Country/Territory	Poland
City	Poznan
Period	3/06/25 → 6/06/25
Internet address	https://www.eucnc.eu/

Access to Document

10.1109/EuCNC/6GSummit63408.2025.11037133Licence: Unspecified

Cite this

Leitch, S., Ahmed, Q., Fontaine, J., Herbruggen, B. V., Shahid, A., De Poorter, E., & Lazaridis, P. (2025). Using Machine Learning to Localize BLE Devices on a Single Anchor. In 2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) (pp. 229-234). Article 11037133 (European Conference on Networks and Communications). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EuCNC/6GSummit63408.2025.11037133

@inproceedings{9102d1dccd0543ab8663bb6d7343ad4e,

title = "Using Machine Learning to Localize BLE Devices on a Single Anchor",

abstract = "Indoor 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.",

keywords = "Indoor Localization, Bluetooth Low Energy, Machine Learning, Angle of Arrival, IoT, Indoor positioning",

author = "Samuel Leitch and Qasim Ahmed and Jaron Fontaine and Herbruggen, \{Ben Van\} and Adnan Shahid and \{De Poorter\}, Eli and Pavlos Lazaridis",

note = "Funding Information: This 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. Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 Joint European Conference on Networks and Communications \& 6G Summit , EuCNC/6G Summit ; Conference date: 03-06-2025 Through 06-06-2025",

year = "2025",

month = jun,

day = "26",

doi = "10.1109/EuCNC/6GSummit63408.2025.11037133",

language = "English",

isbn = "9798350391817",

series = "European Conference on Networks and Communications",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "229--234",

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Leitch, S, Ahmed, Q, Fontaine, J, Herbruggen, BV, Shahid, A, De Poorter, E & Lazaridis, P 2025, Using Machine Learning to Localize BLE Devices on a Single Anchor. in 2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)., 11037133, European Conference on Networks and Communications, Institute of Electrical and Electronics Engineers Inc., pp. 229-234, 2025 Joint European Conference on Networks and Communications & 6G Summit , Poznan, Poland, 3/06/25. https://doi.org/10.1109/EuCNC/6GSummit63408.2025.11037133

Using Machine Learning to Localize BLE Devices on a Single Anchor. / Leitch, Samuel; Ahmed, Qasim; Fontaine, Jaron et al.
2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit). Institute of Electrical and Electronics Engineers Inc., 2025. p. 229-234 11037133 (European Conference on Networks and Communications).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using Machine Learning to Localize BLE Devices on a Single Anchor

AU - Leitch, Samuel

AU - Ahmed, Qasim

AU - Fontaine, Jaron

AU - Herbruggen, Ben Van

AU - Shahid, Adnan

AU - De Poorter, Eli

AU - Lazaridis, Pavlos

N1 - Funding Information: This 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. Publisher Copyright: © 2025 IEEE.

PY - 2025/6/26

Y1 - 2025/6/26

N2 - Indoor 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.

AB - Indoor 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.

KW - Indoor Localization

KW - Bluetooth Low Energy

KW - Machine Learning

KW - Angle of Arrival

KW - IoT

KW - Indoor positioning

UR - https://www.scopus.com/pages/publications/105010652244

U2 - 10.1109/EuCNC/6GSummit63408.2025.11037133

DO - 10.1109/EuCNC/6GSummit63408.2025.11037133

M3 - Conference contribution

SN - 9798350391817

T3 - European Conference on Networks and Communications

SP - 229

EP - 234

BT - 2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2025 Joint European Conference on Networks and Communications & 6G Summit

Y2 - 3 June 2025 through 6 June 2025

ER -

Leitch S, Ahmed Q, Fontaine J, Herbruggen BV, Shahid A, De Poorter E et al. Using Machine Learning to Localize BLE Devices on a Single Anchor. In 2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit). Institute of Electrical and Electronics Engineers Inc. 2025. p. 229-234. 11037133. (European Conference on Networks and Communications). doi: 10.1109/EuCNC/6GSummit63408.2025.11037133

Using Machine Learning to Localize BLE Devices on a Single Anchor

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