构建蓝牙定位数据集，实现高精度角度与距离估计
Bluetooth Low Energy Dataset Using In-Phase and Quadrature Samples for Indoor Localization
- 用相位差技术采集蓝牙信号，自动标注标签用于监督学习
- 角度估计误差仅25.71度，距离估计误差0.174米
- 适合做室内定位、无线传感网络研究者参考
研究面临的一大挑战是收集大量数据并学习输入与输出变量之间的内在关系。本文详细描述了为实现蓝牙低功耗（BLE）技术下的到达角（AoA）估计而构建的数据集的采集与验证过程。数据在实验室环境中采集，旨在模拟真实工业场景。论文讨论了数据采集流程、数据集结构及用于自动化样本标注的监督学习方法。通过德州仪器（TI）的相位差到达（PDoA）实现对数据进行验证，在无遮挡条件下某高度上的平均绝对误差（MAE）为25.71°。采用高斯过程回归算法实现BLE距离估计，结果达到0.174米的MAE。
原文摘要 · Abstract (English)
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.
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