Using machine learning to localize BLE devices on a single anchor
- Author
- Samuel G. Leitch, Qasim Zeeshan Ahmed, Jaron Fontaine (UGent) , Ben Van Herbruggen, Adnan Shahid (UGent) , Eli De Poorter (UGent) and Pavlos Lazaridis
- Organization
- Project
- 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 0.34m on the test scenario.
- Keywords
- INDOOR, SYSTEM, PHASE, Indoor Localization, Bluetooth Low Energy, Machine Learning, Angle of Arrival, IoT, Indoor Positioning
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01K99MN87W4N1ZRQPTDN03YCB5
- MLA
- Leitch, Samuel G., et al. “Using Machine Learning to Localize BLE Devices on a Single Anchor.” 2025 JOINT EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS & 6G SUMMIT, EUCNC/6G SUMMIT, IEEE, 2025, pp. 229–34, doi:10.1109/EUCNC/6GSUMMIT63408.2025.11037133.
- APA
- Leitch, S. G., Ahmed, Q. Z., Fontaine, J., Van Herbruggen, B., Shahid, A., De Poorter, E., & Lazaridis, P. (2025). Using machine learning to localize BLE devices on a single anchor. 2025 JOINT EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS & 6G SUMMIT, EUCNC/6G SUMMIT, 229–234. https://doi.org/10.1109/EUCNC/6GSUMMIT63408.2025.11037133
- Chicago author-date
- Leitch, Samuel G., Qasim Zeeshan Ahmed, Jaron Fontaine, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter, and Pavlos Lazaridis. 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, 229–34. IEEE. https://doi.org/10.1109/EUCNC/6GSUMMIT63408.2025.11037133.
- Chicago author-date (all authors)
- Leitch, Samuel G., Qasim Zeeshan Ahmed, Jaron Fontaine, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter, and Pavlos Lazaridis. 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, 229–234. IEEE. doi:10.1109/EUCNC/6GSUMMIT63408.2025.11037133.
- Vancouver
- 1.Leitch SG, Ahmed QZ, Fontaine J, Van Herbruggen B, 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. IEEE; 2025. p. 229–34.
- IEEE
- [1]S. G. Leitch 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, Poznan, Poland, 2025, pp. 229–234.
@inproceedings{01K99MN87W4N1ZRQPTDN03YCB5,
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 0.34m on the test scenario.}},
author = {{Leitch, Samuel G. and Ahmed, Qasim Zeeshan and Fontaine, Jaron and Van Herbruggen, Ben and Shahid, Adnan and De Poorter, Eli and Lazaridis, Pavlos}},
booktitle = {{2025 JOINT EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS & 6G SUMMIT, EUCNC/6G SUMMIT}},
isbn = {{9798350391817}},
issn = {{2475-6490}},
keywords = {{INDOOR,SYSTEM,PHASE,Indoor Localization,Bluetooth Low Energy,Machine Learning,Angle of Arrival,IoT,Indoor Positioning}},
language = {{eng}},
location = {{Poznan, Poland}},
pages = {{229--234}},
publisher = {{IEEE}},
title = {{Using machine learning to localize BLE devices on a single anchor}},
url = {{http://doi.org/10.1109/EUCNC/6GSUMMIT63408.2025.11037133}},
year = {{2025}},
}
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