Bluetooth Low Energy Dataset Using In-Phase and Quadrature Samples for Indoor Localization
This provides a dataset for researchers working on indoor localization in industrial settings, but it is incremental as it focuses on data collection and validation using existing methods.
The paper tackles indoor localization by creating a Bluetooth Low Energy dataset with in-phase and quadrature samples for angle of arrival estimation, achieving a mean absolute error of 25.71° for angle and 0.174m for distance using Gaussian Process Regression.
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 $25.71^\circ$. The distance estimation on BLE was implemented using a Gaussian Process Regression algorithm, yielding an MAE of $0.174$m.