NOTE: This repository is work in progress and more data, code, and instructions will be added over the following days/weeks.
This repository contains the source code and datasets of our paper Self-Localization of Ultra-wideband Anchors: From Theory to Practice published at IEEE Access and openly available at https://ieeexplore.ieee.org/abstract/document/10080967.
In this paper, we look at the problem of finding the positions of UWB anchors in large spaces via self-localization. To this end, we exploit a multidimensional scaling (MDS) algorithm, built atop Python scikit-learn library, to estimate the positions of the anchor devices deployed in three real-world, large-scale, multi-hop UWB testbeds. These testbeds are:
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PLANT: A 28-anchor deployment in a large industrial plant, covering a rectangular area of ∼3000 m2. It is characterized by the presence of metallic objects and NLoS conditions, typical of industrial settings.
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DEPARTMENT: A 36-anchor deployment spanning an entire floor at the University of Trento. The anchors cover an area of 80 m × 40 m, but are deployed mostly along corridors, which are very narrow (2.7 m) and long. This yields a very challenging geometry for localization, yet representative of many indoor applications.
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RECEPTION: A 19-anchor deployment covering a total of 720 m2 in the reception floor of the University of Trento. The area is L-shaped, with two nearly-separated areas connected only by a few NLoS links.
The true positions of the anchor nodes can be found in selfloc/sw/deployments.
DEPARTMENT and RECEPTION are now part of the public CLOVES IoT Testbed at the University of Trento, which you can use right away following this page.
.
├── README.md
├── data: ranging and connectivity data from the the three testbeds
│ ├── department
│ ├── plant
│ └── reception
├── img: image files used in this README.md and clean images to draw testbed maps
└── sw
├── deployments: true positions of testbed nodes
Please consider citing our IEEE Access paper if you use the data or code provided in this repository.
@article{selfloc,
author={Corbal\'{a}n, Pablo and Picco, Gian Pietro and Coors, Martin and Jain, Vivek},
journal={IEEE Access},
title={{Self-Localization of Ultra-Wideband Anchors: From Theory to Practice}},
year={2023},
volume={11},
number={},
pages={29711-29725},
doi={10.1109/ACCESS.2023.3261567}
}
We take no responsibility for and give no warranties in respect of using the data and code provided in this repository.