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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publication
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/87834/ http://psasir.upm.edu.my/id/eprint/87834/1/ABSTRACT.pdf |
| _version_ | 1848860509594451968 |
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| author | Khan, Imran Ullah Ali, Tariq Farid, Zahid Scavino, Edgar Abd Rahman, Mohd Amiruddin Hamdi, Mohammed Qiao, Gang |
| author_facet | Khan, Imran Ullah Ali, Tariq Farid, Zahid Scavino, Edgar Abd Rahman, Mohd Amiruddin Hamdi, Mohammed Qiao, Gang |
| author_sort | Khan, Imran Ullah |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T12:46:22Z |
| format | Article |
| id | upm-87834 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:46:22Z |
| publishDate | 2020 |
| publisher | SAGE Publication |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/87834/ http://psasir.upm.edu.my/id/eprint/87834/ http://psasir.upm.edu.my/id/eprint/87834/ http://psasir.upm.edu.my/id/eprint/87834/1/ABSTRACT.pdf |