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

TL;DR

This paper presents a BLE dataset for indoor localization using AoA and distance estimation, validated with ground truth labels and machine learning, to improve indoor positioning accuracy.

Contribution

It introduces a comprehensive BLE dataset with ground truth labels for AoA and distance, and demonstrates validation methods and machine learning applications for indoor localization.

Findings

Mean absolute error for AoA estimation: 25.71 degrees

Distance estimation MAE: 0.174 meters

Validation using TI PDoA implementation

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 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.