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Deep Learning-Based Indoor Localization Using Multi-View BLE Signal
In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) va...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003244/ https://www.ncbi.nlm.nih.gov/pubmed/35408373 http://dx.doi.org/10.3390/s22072759 |
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author | Koutris, Aristotelis Siozos, Theodoros Kopsinis, Yannis Pikrakis, Aggelos Merk, Timon Mahlig, Matthias Papaharalabos, Stylianos Karlsson, Peter |
author_facet | Koutris, Aristotelis Siozos, Theodoros Kopsinis, Yannis Pikrakis, Aggelos Merk, Timon Mahlig, Matthias Papaharalabos, Stylianos Karlsson, Peter |
author_sort | Koutris, Aristotelis |
collection | PubMed |
description | In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs. |
format | Online Article Text |
id | pubmed-9003244 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90032442022-04-13 Deep Learning-Based Indoor Localization Using Multi-View BLE Signal Koutris, Aristotelis Siozos, Theodoros Kopsinis, Yannis Pikrakis, Aggelos Merk, Timon Mahlig, Matthias Papaharalabos, Stylianos Karlsson, Peter Sensors (Basel) Article In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs. MDPI 2022-04-02 /pmc/articles/PMC9003244/ /pubmed/35408373 http://dx.doi.org/10.3390/s22072759 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Koutris, Aristotelis Siozos, Theodoros Kopsinis, Yannis Pikrakis, Aggelos Merk, Timon Mahlig, Matthias Papaharalabos, Stylianos Karlsson, Peter Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
title | Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
title_full | Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
title_fullStr | Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
title_full_unstemmed | Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
title_short | Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
title_sort | deep learning-based indoor localization using multi-view ble signal |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003244/ https://www.ncbi.nlm.nih.gov/pubmed/35408373 http://dx.doi.org/10.3390/s22072759 |
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