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Bluetooth low energy dataset using in-phase and quadrature samples for indoor localization

Samuel G. Leitch, Qasim Zeeshan Ahmed, Ben Van Herbruggen, Mathias Baert (UGent) , Jaron Fontaine (UGent) , Eli De Poorter (UGent) , Adnan Shahid (UGent) and Pavlos I. Lazaridis
(2025) IEEE SENSORS JOURNAL. 25(3). p.5668-5678
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Abstract
One significant challenge in research is to collect a large amount of data and learn the underlying relationship between the input and the output variables. This article outlines the process of collecting and validating a dataset designed to determine the angle of arrival (AoA) using Bluetooth low energy (BLE) technology. The data, collected in a laboratory setting, are intended to approximate real-world industrial scenarios. This article discusses the data collection process, the structure of the dataset, and the methodology adopted for automating sample labeling for supervised learning. The collected samples and the process of generating ground-truth (GT) labels were validated using the Texas instruments (TIs) phase difference of arrival (PDoA) implementation on the data, yielding a mean absolute error (MAE) at one of the heights without obstacles of 25.71 degrees. The distance estimation on BLE was implemented using a Gaussian process regression algorithm, yielding an MAE of 0.174 m.
Keywords
Sensors, Location awareness, Hardware, Accuracy, IP networks, Antenna measurements, Antenna arrays, Wireless communication, Arrays, Bluetooth Low Energy, Bluetooth low energy (BLE), dataset, indoor positioning (IP), phase difference of arrival (PDoA), uniform linear array (ULA), RADIO

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MLA
Leitch, Samuel G., et al. “Bluetooth Low Energy Dataset Using In-Phase and Quadrature Samples for Indoor Localization.” IEEE SENSORS JOURNAL, vol. 25, no. 3, 2025, pp. 5668–78, doi:10.1109/JSEN.2024.3510213.
APA
Leitch, S. G., Ahmed, Q. Z., Van Herbruggen, B., Baert, M., Fontaine, J., De Poorter, E., … Lazaridis, P. I. (2025). Bluetooth low energy dataset using in-phase and quadrature samples for indoor localization. IEEE SENSORS JOURNAL, 25(3), 5668–5678. https://doi.org/10.1109/JSEN.2024.3510213
Chicago author-date
Leitch, Samuel G., Qasim Zeeshan Ahmed, Ben Van Herbruggen, Mathias Baert, Jaron Fontaine, Eli De Poorter, Adnan Shahid, and Pavlos I. Lazaridis. 2025. “Bluetooth Low Energy Dataset Using In-Phase and Quadrature Samples for Indoor Localization.” IEEE SENSORS JOURNAL 25 (3): 5668–78. https://doi.org/10.1109/JSEN.2024.3510213.
Chicago author-date (all authors)
Leitch, Samuel G., Qasim Zeeshan Ahmed, Ben Van Herbruggen, Mathias Baert, Jaron Fontaine, Eli De Poorter, Adnan Shahid, and Pavlos I. Lazaridis. 2025. “Bluetooth Low Energy Dataset Using In-Phase and Quadrature Samples for Indoor Localization.” IEEE SENSORS JOURNAL 25 (3): 5668–5678. doi:10.1109/JSEN.2024.3510213.
Vancouver
1.
Leitch SG, Ahmed QZ, Van Herbruggen B, Baert M, Fontaine J, De Poorter E, et al. Bluetooth low energy dataset using in-phase and quadrature samples for indoor localization. IEEE SENSORS JOURNAL. 2025;25(3):5668–78.
IEEE
[1]
S. G. Leitch et al., “Bluetooth low energy dataset using in-phase and quadrature samples for indoor localization,” IEEE SENSORS JOURNAL, vol. 25, no. 3, pp. 5668–5678, 2025.
@article{01JMVRH5Q2JCG1PPAQFPVYDA3G,
  abstract     = {{One significant challenge in research is to collect a large amount of data and learn the underlying relationship between the input and the output variables. This article outlines the process of collecting and validating a dataset designed to determine the angle of arrival (AoA) using Bluetooth low energy (BLE) technology. The data, collected in a laboratory setting, are intended to approximate real-world industrial scenarios. This article discusses the data collection process, the structure of the dataset, and the methodology adopted for automating sample labeling for supervised learning. The collected samples and the process of generating ground-truth (GT) labels were validated using the Texas instruments (TIs) phase difference of arrival (PDoA) implementation on the data, yielding a mean absolute error (MAE) at one of the heights without obstacles of 25.71 degrees. The distance estimation on BLE was implemented using a Gaussian process regression algorithm, yielding an MAE of 0.174 m.}},
  author       = {{Leitch, Samuel G. and Ahmed, Qasim Zeeshan and Van Herbruggen, Ben and Baert, Mathias and Fontaine, Jaron and De Poorter, Eli and Shahid, Adnan and Lazaridis, Pavlos I.}},
  issn         = {{1530-437X}},
  journal      = {{IEEE SENSORS JOURNAL}},
  keywords     = {{Sensors,Location awareness,Hardware,Accuracy,IP networks,Antenna measurements,Antenna arrays,Wireless communication,Arrays,Bluetooth Low Energy,Bluetooth low energy (BLE),dataset,indoor positioning (IP),phase difference of arrival (PDoA),uniform linear array (ULA),RADIO}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{5668--5678}},
  title        = {{Bluetooth low energy dataset using in-phase and quadrature samples for indoor localization}},
  url          = {{http://doi.org/10.1109/JSEN.2024.3510213}},
  volume       = {{25}},
  year         = {{2025}},
}
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