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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...

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Autores principales: Koutris, Aristotelis, Siozos, Theodoros, Kopsinis, Yannis, Pikrakis, Aggelos, Merk, Timon, Mahlig, Matthias, Papaharalabos, Stylianos, Karlsson, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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