TY - JOUR
T1 - Bluetooth Low Energy Dataset Using In-Phase and Quadrature Samples for Indoor Localization
AU - Leitch, Samuel
AU - Ahmed, Qasim
AU - Herbruggen, Ben Van
AU - Baert, Mathias
AU - Fontaine, Jaron
AU - De Poorter, Eli
AU - Shahid, Adnan
AU - Lazaridis, Pavlos
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2024/11/20
Y1 - 2024/11/20
N2 - 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 paper 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, is intended to approximate real-world industrial scenarios. This paper 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 (TI) 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°. The distance estimation on BLE was implemented using a Gaussian Process Regression algorithm, yielding an MAE of 0.174m.
AB - 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 paper 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, is intended to approximate real-world industrial scenarios. This paper 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 (TI) 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°. The distance estimation on BLE was implemented using a Gaussian Process Regression algorithm, yielding an MAE of 0.174m.
KW - Bluetooth Low Energy (BLE)
KW - Indoor Positioning (IP)
KW - Dataset
KW - Phase Difference of Arrival (PDoA)
KW - Uniform Linear Array (ULA)
UR - http://www.scopus.com/inward/record.url?scp=85212547846&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2024.3510213
DO - 10.1109/JSEN.2024.3510213
M3 - Article
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
SN - 1530-437X
M1 - 10794607
ER -
