A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks
Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/3/776 |
| _version_ | 1797484858636763136 |
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| author | Yuh-Shyan Chen Chih-Shun Hsu Ren-Shao Chung |
| author_facet | Yuh-Shyan Chen Chih-Shun Hsu Ren-Shao Chung |
| author_sort | Yuh-Shyan Chen |
| collection | DOAJ |
| description | Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user’s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment. |
| first_indexed | 2024-03-09T23:10:32Z |
| format | Article |
| id | doaj.art-2f2f1d844462423a962ffdef74178be1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:10:32Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2f2f1d844462423a962ffdef74178be12023-11-23T17:45:11ZengMDPI AGSensors1424-82202022-01-0122377610.3390/s22030776A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi NetworksYuh-Shyan Chen0Chih-Shun Hsu1Ren-Shao Chung2Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 237, TaiwanDepartment of Information Management, Shih Hsin University, No. 1, Ln. 17, Sec. 1, Muzha Rd., Wenshan District, Taipei City 116, TaiwanDepartment of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 237, TaiwanIndoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user’s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment.https://www.mdpi.com/1424-8220/22/3/7763D indoor localizationmulti-kernelWiFitransfer learningsemi-supervised learning |
| spellingShingle | Yuh-Shyan Chen Chih-Shun Hsu Ren-Shao Chung A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks Sensors 3D indoor localization multi-kernel WiFi transfer learning semi-supervised learning |
| title | A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks |
| title_full | A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks |
| title_fullStr | A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks |
| title_full_unstemmed | A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks |
| title_short | A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks |
| title_sort | semi supervised 3d indoor localization using multi kernel learning for wifi networks |
| topic | 3D indoor localization multi-kernel WiFi transfer learning semi-supervised learning |
| url | https://www.mdpi.com/1424-8220/22/3/776 |
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