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Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization

Bluetooth Low-Energy (BLE) beacons-based indoor positioning is a promising method for indoor positioning, especially in applications of position-based services (PbS). It has low deployment cost and it is suitable for a wide range of mobile devices. Existing BLE beacon-based positioning methods can b...

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Autores principales: Zuo, Zheng, Liu, Liang, Zhang, Lei, Fang, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264008/
https://www.ncbi.nlm.nih.gov/pubmed/30400185
http://dx.doi.org/10.3390/s18113736
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author Zuo, Zheng
Liu, Liang
Zhang, Lei
Fang, Yong
author_facet Zuo, Zheng
Liu, Liang
Zhang, Lei
Fang, Yong
author_sort Zuo, Zheng
collection PubMed
description Bluetooth Low-Energy (BLE) beacons-based indoor positioning is a promising method for indoor positioning, especially in applications of position-based services (PbS). It has low deployment cost and it is suitable for a wide range of mobile devices. Existing BLE beacon-based positioning methods can be categorized as range-based methods and fingerprinting-based methods. For range-based methods, the positions of the beacons should be known before positioning. For fingerprinting-based methods, a pre-requisite is the reference fingerprinting map (RFM). Many existing methods focus on how to perform the positioning assuming the beacon positions or RFM are known. However, in practical applications, determining the beacon positions or RFM in the indoor environment is normally a difficult task. This paper proposed an efficient and graph optimization-based way for estimating the beacon positions and the RFM, which combines the range-based method and the fingerprinting-based method. The method exists without need for any dedicated surveying instruments. A user equipped with a BLE-enabled mobile device walks in the region collecting inertial readings and BLE received signal strength indication (RSSI) readings. The inertial measurements are processed through the pedestrian dead reckoning (PDR) method to generate the constraints at adjacent poses. In addition, the BLE fingerprints are adopted to generate constraints between poses (with similar fingerprints) and the RSSIs are adopted to generate distance constraints between the poses and the beacon positions (according to a pre-defined path-loss model). The constraints are then adopted to form a cost function with a least square structure. By minimizing the cost function, the optimal user poses at different times and the beacon positions are estimated. In addition, the RFM can be generated through the pose estimations. Experiments are carried out, which validates that the proposed method for estimating the pre-requisites (including beacon positions and the RFM). These estimated pre-requisites are of sufficient quality for both range-based and fingerprinting-based positioning.
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spelling pubmed-62640082018-12-12 Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization Zuo, Zheng Liu, Liang Zhang, Lei Fang, Yong Sensors (Basel) Article Bluetooth Low-Energy (BLE) beacons-based indoor positioning is a promising method for indoor positioning, especially in applications of position-based services (PbS). It has low deployment cost and it is suitable for a wide range of mobile devices. Existing BLE beacon-based positioning methods can be categorized as range-based methods and fingerprinting-based methods. For range-based methods, the positions of the beacons should be known before positioning. For fingerprinting-based methods, a pre-requisite is the reference fingerprinting map (RFM). Many existing methods focus on how to perform the positioning assuming the beacon positions or RFM are known. However, in practical applications, determining the beacon positions or RFM in the indoor environment is normally a difficult task. This paper proposed an efficient and graph optimization-based way for estimating the beacon positions and the RFM, which combines the range-based method and the fingerprinting-based method. The method exists without need for any dedicated surveying instruments. A user equipped with a BLE-enabled mobile device walks in the region collecting inertial readings and BLE received signal strength indication (RSSI) readings. The inertial measurements are processed through the pedestrian dead reckoning (PDR) method to generate the constraints at adjacent poses. In addition, the BLE fingerprints are adopted to generate constraints between poses (with similar fingerprints) and the RSSIs are adopted to generate distance constraints between the poses and the beacon positions (according to a pre-defined path-loss model). The constraints are then adopted to form a cost function with a least square structure. By minimizing the cost function, the optimal user poses at different times and the beacon positions are estimated. In addition, the RFM can be generated through the pose estimations. Experiments are carried out, which validates that the proposed method for estimating the pre-requisites (including beacon positions and the RFM). These estimated pre-requisites are of sufficient quality for both range-based and fingerprinting-based positioning. MDPI 2018-11-02 /pmc/articles/PMC6264008/ /pubmed/30400185 http://dx.doi.org/10.3390/s18113736 Text en © 2018 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
Zuo, Zheng
Liu, Liang
Zhang, Lei
Fang, Yong
Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization
title Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization
title_full Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization
title_fullStr Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization
title_full_unstemmed Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization
title_short Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization
title_sort indoor positioning based on bluetooth low-energy beacons adopting graph optimization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264008/
https://www.ncbi.nlm.nih.gov/pubmed/30400185
http://dx.doi.org/10.3390/s18113736
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AT fangyong indoorpositioningbasedonbluetoothlowenergybeaconsadoptinggraphoptimization