Harvesting Indoor Positioning Accuracy by Exploring Multiple Features From Received Signal Strength Vector
The development of an indoor location information system using ubiquitous resources available in the environment is a challenging problem in the field of Geo-Location technologies, these days. Therefore, instead of relying on a single resource, the fusion of location information from multiple resour...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8695172/ |
| _version_ | 1818854677062615040 |
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| author | Muhammad Usman Ali Soojung Hur Sangjoon Park Yongwan Park |
| author_facet | Muhammad Usman Ali Soojung Hur Sangjoon Park Yongwan Park |
| author_sort | Muhammad Usman Ali |
| collection | DOAJ |
| description | The development of an indoor location information system using ubiquitous resources available in the environment is a challenging problem in the field of Geo-Location technologies, these days. Therefore, instead of relying on a single resource, the fusion of location information from multiple resources into an indoor positioning system (IPS) becomes important. The IPS in which information from multiple sources such as Wi-Fi, geomagnetism, and motion sensors is fused to harvest the next level of accuracy is commonly known as hybrid IPS. The initial estimate of the position with high accuracy is very critical for the hybrid IPS. Wi-Fi fingerprinting is one of the potential candidates for providing the initial position in such systems, whereas due to the multipath, absorption, and fading characteristics of the indoor environment, the accuracy of the Wi-Fi fingerprinting techniques is limited. Many algorithms and techniques have been proposed to improve the accuracy of Wi-Fi-based IPSs. However, most of the solution requires high computing resources and specialized hardware. This article proposes an empirical approach in which the important features present in the received signal strength vector (RSSV) of the Wi-Fi device are selected to exploit the similarity measure and index order of the Access Points (APs). The experimental results show that these features make it possible to avoid long distances outliers and to improve the positioning accuracy of the Wi-Fi fingerprinting technique without the use of specialized hardware. |
| first_indexed | 2024-12-19T07:56:30Z |
| format | Article |
| id | doaj.art-e5e974ced7e146a790320ea7724c5a19 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:56:30Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e5e974ced7e146a790320ea7724c5a192022-12-21T20:30:00ZengIEEEIEEE Access2169-35362019-01-017521105212110.1109/ACCESS.2019.29116018695172Harvesting Indoor Positioning Accuracy by Exploring Multiple Features From Received Signal Strength VectorMuhammad Usman Ali0https://orcid.org/0000-0002-4470-8065Soojung Hur1Sangjoon Park2Yongwan Park3Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaElectronics and Telecommunications Research Institute, Daejeon, South KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaThe development of an indoor location information system using ubiquitous resources available in the environment is a challenging problem in the field of Geo-Location technologies, these days. Therefore, instead of relying on a single resource, the fusion of location information from multiple resources into an indoor positioning system (IPS) becomes important. The IPS in which information from multiple sources such as Wi-Fi, geomagnetism, and motion sensors is fused to harvest the next level of accuracy is commonly known as hybrid IPS. The initial estimate of the position with high accuracy is very critical for the hybrid IPS. Wi-Fi fingerprinting is one of the potential candidates for providing the initial position in such systems, whereas due to the multipath, absorption, and fading characteristics of the indoor environment, the accuracy of the Wi-Fi fingerprinting techniques is limited. Many algorithms and techniques have been proposed to improve the accuracy of Wi-Fi-based IPSs. However, most of the solution requires high computing resources and specialized hardware. This article proposes an empirical approach in which the important features present in the received signal strength vector (RSSV) of the Wi-Fi device are selected to exploit the similarity measure and index order of the Access Points (APs). The experimental results show that these features make it possible to avoid long distances outliers and to improve the positioning accuracy of the Wi-Fi fingerprinting technique without the use of specialized hardware.https://ieeexplore.ieee.org/document/8695172/Indoor positioningIPSparticle swarm optimizationPSOreceived signal strengthRSSI |
| spellingShingle | Muhammad Usman Ali Soojung Hur Sangjoon Park Yongwan Park Harvesting Indoor Positioning Accuracy by Exploring Multiple Features From Received Signal Strength Vector IEEE Access Indoor positioning IPS particle swarm optimization PSO received signal strength RSSI |
| title | Harvesting Indoor Positioning Accuracy by Exploring Multiple Features From Received Signal Strength Vector |
| title_full | Harvesting Indoor Positioning Accuracy by Exploring Multiple Features From Received Signal Strength Vector |
| title_fullStr | Harvesting Indoor Positioning Accuracy by Exploring Multiple Features From Received Signal Strength Vector |
| title_full_unstemmed | Harvesting Indoor Positioning Accuracy by Exploring Multiple Features From Received Signal Strength Vector |
| title_short | Harvesting Indoor Positioning Accuracy by Exploring Multiple Features From Received Signal Strength Vector |
| title_sort | harvesting indoor positioning accuracy by exploring multiple features from received signal strength vector |
| topic | Indoor positioning IPS particle swarm optimization PSO received signal strength RSSI |
| url | https://ieeexplore.ieee.org/document/8695172/ |
| work_keys_str_mv | AT muhammadusmanali harvestingindoorpositioningaccuracybyexploringmultiplefeaturesfromreceivedsignalstrengthvector AT soojunghur harvestingindoorpositioningaccuracybyexploringmultiplefeaturesfromreceivedsignalstrengthvector AT sangjoonpark harvestingindoorpositioningaccuracybyexploringmultiplefeaturesfromreceivedsignalstrengthvector AT yongwanpark harvestingindoorpositioningaccuracybyexploringmultiplefeaturesfromreceivedsignalstrengthvector |