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Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons

We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A novel aspect of our approach is th...

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Detalles Bibliográficos
Autores principales: Daniş, F. Serhan, Cemgil, Ali Taylan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713487/
https://www.ncbi.nlm.nih.gov/pubmed/29109375
http://dx.doi.org/10.3390/s17112484
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author Daniş, F. Serhan
Cemgil, Ali Taylan
author_facet Daniş, F. Serhan
Cemgil, Ali Taylan
author_sort Daniş, F. Serhan
collection PubMed
description We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A novel aspect of our approach is the development of an observation model, specifically tailored for received signal strength indicator (RSSI) fingerprints: a combination based on the optimal transport model of Wasserstein distance. The tracking results of the entire system are compared with alternative baseline estimation methods, such as nearest neighboring fingerprints and an artificial neural network. Our results show that highly accurate estimation from noisy Bluetooth data is practically feasible with an observation model based on Wasserstein distance interpolation combined with the sequential Monte Carlo (SMC) method for tracking.
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spelling pubmed-57134872017-12-07 Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons Daniş, F. Serhan Cemgil, Ali Taylan Sensors (Basel) Article We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A novel aspect of our approach is the development of an observation model, specifically tailored for received signal strength indicator (RSSI) fingerprints: a combination based on the optimal transport model of Wasserstein distance. The tracking results of the entire system are compared with alternative baseline estimation methods, such as nearest neighboring fingerprints and an artificial neural network. Our results show that highly accurate estimation from noisy Bluetooth data is practically feasible with an observation model based on Wasserstein distance interpolation combined with the sequential Monte Carlo (SMC) method for tracking. MDPI 2017-10-29 /pmc/articles/PMC5713487/ /pubmed/29109375 http://dx.doi.org/10.3390/s17112484 Text en © 2017 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Daniş, F. Serhan
Cemgil, Ali Taylan
Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons
title Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons
title_full Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons
title_fullStr Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons
title_full_unstemmed Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons
title_short Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons
title_sort model-based localization and tracking using bluetooth low-energy beacons
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713487/
https://www.ncbi.nlm.nih.gov/pubmed/29109375
http://dx.doi.org/10.3390/s17112484
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