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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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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. |
format | Online Article Text |
id | pubmed-5713487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT danisfserhan modelbasedlocalizationandtrackingusingbluetoothlowenergybeacons AT cemgilalitaylan modelbasedlocalizationandtrackingusingbluetoothlowenergybeacons |