Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy

Indoor positioning is getting increased attention due to the availability of larger and more sophisticated indoor environments. Wireless technologies like Bluetooth Low Energy (BLE) may provide inexpensive solutions. In this paper, we propose obstruction-aware signal-loss-tolerant indoor positioning...

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Bibliographic Details
Main Authors: Aybars Kerem Taşkan, Hande Alemdar
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/21/3/971
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spelling doaj-97f36e7ff37b4e3c8830a8f8a95d38262021-02-02T00:03:02ZengMDPI AGSensors1424-82202021-02-012197197110.3390/s21030971Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low EnergyAybars Kerem Taşkan0Hande Alemdar1Middle East Technical University Department of Computer Engineering, Çankaya/ Ankara 06800, TurkeyMiddle East Technical University Department of Computer Engineering, Çankaya/ Ankara 06800, TurkeyIndoor positioning is getting increased attention due to the availability of larger and more sophisticated indoor environments. Wireless technologies like Bluetooth Low Energy (BLE) may provide inexpensive solutions. In this paper, we propose obstruction-aware signal-loss-tolerant indoor positioning (OASLTIP), a cost-effective BLE-based indoor positioning algorithm. OASLTIP uses a combination of techniques together to provide optimum tracking performance by taking into account the obstructions in the environment, and also, it can handle a loss of signal. We use running average filtering to smooth the received signal data, multilateration to find the measured position of the tag, and particle filtering to track the tag for better performance. We also propose an optional receiver placement method and provide the option to use fingerprinting together with OASLTIP. Moreover, we give insights about BLE signal strengths in different conditions to help with understanding the effects of some environmental conditions on BLE signals. We performed extensive experiments for evaluation of the OASLTool we developed. Additionally, we evaluated the performance of the system both in a simulated environment and in real-world conditions. In a highly crowded and occluded office environment, our system achieved 2.29 m average error, with three receivers. When simulated in OASLTool, the same setup yielded an error of 2.58 m.https://www.mdpi.com/1424-8220/21/3/971indoor positioningBluetooth Low Energyparticle filtermultilateration
collection DOAJ
language English
format Article
sources DOAJ
author Aybars Kerem Taşkan
Hande Alemdar
spellingShingle Aybars Kerem Taşkan
Hande Alemdar
Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy
Sensors
indoor positioning
Bluetooth Low Energy
particle filter
multilateration
author_facet Aybars Kerem Taşkan
Hande Alemdar
author_sort Aybars Kerem Taşkan
title Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy
title_short Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy
title_full Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy
title_fullStr Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy
title_full_unstemmed Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy
title_sort obstruction-aware signal-loss-tolerant indoor positioning using bluetooth low energy
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2021-02-01
description Indoor positioning is getting increased attention due to the availability of larger and more sophisticated indoor environments. Wireless technologies like Bluetooth Low Energy (BLE) may provide inexpensive solutions. In this paper, we propose obstruction-aware signal-loss-tolerant indoor positioning (OASLTIP), a cost-effective BLE-based indoor positioning algorithm. OASLTIP uses a combination of techniques together to provide optimum tracking performance by taking into account the obstructions in the environment, and also, it can handle a loss of signal. We use running average filtering to smooth the received signal data, multilateration to find the measured position of the tag, and particle filtering to track the tag for better performance. We also propose an optional receiver placement method and provide the option to use fingerprinting together with OASLTIP. Moreover, we give insights about BLE signal strengths in different conditions to help with understanding the effects of some environmental conditions on BLE signals. We performed extensive experiments for evaluation of the OASLTool we developed. Additionally, we evaluated the performance of the system both in a simulated environment and in real-world conditions. In a highly crowded and occluded office environment, our system achieved 2.29 m average error, with three receivers. When simulated in OASLTool, the same setup yielded an error of 2.58 m.
topic indoor positioning
Bluetooth Low Energy
particle filter
multilateration
url https://www.mdpi.com/1424-8220/21/3/971
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