Bluetooth Low Energy Dataset Using In-Phase and Quadrature Samples for Indoor Localization

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 Indoor Positioning (IP), has the potential to enable several different technologies that could massively improve patient management in care homes, asset management and general automation in warehouses, automated inspection and maintenance, etc [1], [2]. IP assists in determining the device location by measuring location-dependent phenomena. These measurements could include the received signal strength (RSS) of wireless signals at the device's location, the angle of arrival (AoA) of wireless signals between the device and a base station, or the time of arrival (ToA) or time difference of arrival (TDoA) of wireless signals [3]. A wide range of wireless technologies are employable for RSS, AoA, ToA, and TDoA measurements. These include, but are not limited to, Bluetooth low energy (BLE) [4], [5], ultra-wideband radio (UWB) [6]-[8], wireless fidelity (WiFi) [9], [10] and millimeter (mm) wave radio [11]-[13]. UWB radio provides extremely accurate ToA and TDoA measurements due to the narrow width of its signals in the time domain, and more recently work has started on applying UWB to the task of AoA determination [14]. However, when considering a large-scale IP system, it is essential to consider the deployment cost of the technology.