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An improved hybrid indoor positioning system based on surface tessellation artificial neural network

In indoor environments, accurate location or positioning becomes an essential requirement, driven by the need for autonomous moving devices, or to identify the position of people in large spaces. Single technology schemes which use WiFi and Bluetooth are affected by fading effects as well as by sign...

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Main Authors: Khan, Imran Ullah, Ali, Tariq, Farid, Zahid, Scavino, Edgar, Abd Rahman, Mohd Amiruddin, Hamdi, Mohammed, Qiao, Gang
Format: Article
Language:English
Published: SAGE Publication 2020
Online Access:http://psasir.upm.edu.my/id/eprint/87834/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/87834/
https://journals.sagepub.com/doi/full/10.1177/0020294020964242
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spelling my.upm.eprints.878342022-06-14T08:24:50Z http://psasir.upm.edu.my/id/eprint/87834/ An improved hybrid indoor positioning system based on surface tessellation artificial neural network Khan, Imran Ullah Ali, Tariq Farid, Zahid Scavino, Edgar Abd Rahman, Mohd Amiruddin Hamdi, Mohammed Qiao, Gang In indoor environments, accurate location or positioning becomes an essential requirement, driven by the need for autonomous moving devices, or to identify the position of people in large spaces. Single technology schemes which use WiFi and Bluetooth are affected by fading effects as well as by signal noise, providing inaccuracies in location estimation. Hybrid locating or positioning schemes have been used in indoor situations and scenarios in order to improve the location accuracy. Hence, this paper proposes a hybrid scheme (technique) to implement fingerprint-based indoor positioning or localization, which uses the Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points as well as Wireless Sensor Networks (WSNs) technologies. Our approach consists of performing a virtual tessellation of the indoor surface, with a set of square tiles encompassing the whole area. The model uses an Artificial Neural Network (ANN) approach for position estimate, in which related RSS is associated to a 1 m × 1 m tile. The ANN was trained to match the RSS signal strength to the corresponding tile. Experimental results indicate that the average distance error, based on tile identification accuracy, is 0.625 m from tile-to-tile, showing a remarkable improvement compared to previous approaches. SAGE Publication 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/87834/1/ABSTRACT.pdf Khan, Imran Ullah and Ali, Tariq and Farid, Zahid and Scavino, Edgar and Abd Rahman, Mohd Amiruddin and Hamdi, Mohammed and Qiao, Gang (2020) An improved hybrid indoor positioning system based on surface tessellation artificial neural network. Measurement and Control, 53 (9-10). pp. 1-10. ISSN 0020-2940 https://journals.sagepub.com/doi/full/10.1177/0020294020964242 10.1177/0020294020964242
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description In indoor environments, accurate location or positioning becomes an essential requirement, driven by the need for autonomous moving devices, or to identify the position of people in large spaces. Single technology schemes which use WiFi and Bluetooth are affected by fading effects as well as by signal noise, providing inaccuracies in location estimation. Hybrid locating or positioning schemes have been used in indoor situations and scenarios in order to improve the location accuracy. Hence, this paper proposes a hybrid scheme (technique) to implement fingerprint-based indoor positioning or localization, which uses the Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points as well as Wireless Sensor Networks (WSNs) technologies. Our approach consists of performing a virtual tessellation of the indoor surface, with a set of square tiles encompassing the whole area. The model uses an Artificial Neural Network (ANN) approach for position estimate, in which related RSS is associated to a 1 m × 1 m tile. The ANN was trained to match the RSS signal strength to the corresponding tile. Experimental results indicate that the average distance error, based on tile identification accuracy, is 0.625 m from tile-to-tile, showing a remarkable improvement compared to previous approaches.
format Article
author Khan, Imran Ullah
Ali, Tariq
Farid, Zahid
Scavino, Edgar
Abd Rahman, Mohd Amiruddin
Hamdi, Mohammed
Qiao, Gang
spellingShingle Khan, Imran Ullah
Ali, Tariq
Farid, Zahid
Scavino, Edgar
Abd Rahman, Mohd Amiruddin
Hamdi, Mohammed
Qiao, Gang
An improved hybrid indoor positioning system based on surface tessellation artificial neural network
author_facet Khan, Imran Ullah
Ali, Tariq
Farid, Zahid
Scavino, Edgar
Abd Rahman, Mohd Amiruddin
Hamdi, Mohammed
Qiao, Gang
author_sort Khan, Imran Ullah
title An improved hybrid indoor positioning system based on surface tessellation artificial neural network
title_short An improved hybrid indoor positioning system based on surface tessellation artificial neural network
title_full An improved hybrid indoor positioning system based on surface tessellation artificial neural network
title_fullStr An improved hybrid indoor positioning system based on surface tessellation artificial neural network
title_full_unstemmed An improved hybrid indoor positioning system based on surface tessellation artificial neural network
title_sort improved hybrid indoor positioning system based on surface tessellation artificial neural network
publisher SAGE Publication
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/87834/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/87834/
https://journals.sagepub.com/doi/full/10.1177/0020294020964242
_version_ 1736835701561884672
score 13.211869

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