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

arXiv: https://arxiv.org/abs/2412.01767 · HTML: https://mlsanity.com/paper/2412.01767/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference](https://mlsanity.com/paper/2001.04061/) (2020)
- [Novel Fine-Tuned Attribute Weighted Na"ive Bayes NLoS Classifier for UWB Positioning](https://mlsanity.com/paper/2304.11067/) (2023)

## Abstract (arXiv)

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 0.174m.