Summary of Bluetooth Low Energy Dataset Using In-phase and Quadrature Samples For Indoor Localization, by Samuel G. Leitch et al.
Bluetooth Low Energy Dataset Using In-Phase and Quadrature Samples for Indoor Localization
by Samuel G. Leitch, Qasim Zeeshan Ahmed, Ben Van Herbruggen, Mathias Baert, Jaron Fontaine, Eli De Poorter, Adnan Shahid, Pavlos I. Lazaridis
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a dataset and methodology for determining the angle of arrival (AoA) using Bluetooth low energy (BLE) technology in laboratory settings. The dataset is designed to mimic real-world industrial scenarios and features samples labeled using ground truth (GT) labels validated by Texas Instruments’ phase difference of arrival (PDoA) implementation, achieving a mean absolute error (MAE) of 25.71^. A Gaussian Process Regression algorithm is used for distance estimation on BLE, resulting in an MAE of 0.174m. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes a big discovery! Scientists are trying to figure out how to use Bluetooth signals to know where things are coming from. They created a special dataset and way to label it so computers can learn from it. The dataset is like a fake real-world scenario, and they tested it using super precise tools. The results show that the method is pretty good at guessing distances (0.174m) and directions (angle of arrival 25.71^). This research could help us use Bluetooth signals in new ways. |
Keywords
» Artificial intelligence » Mae » Regression
