A novel method of adaptive weighted -nearest neighbor fingerprint indoor positioning considering user’s orientation
There are many factors affecting Wi-Fi signal in indoor environment, among which the human body has an important impact. And, its characteristic is related to the user’s orientation. To eliminate positioning errors caused by user’s human body and improve positioning accuracy, this study puts forward...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718785885 |
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| _version_ | 1832547222316646400 |
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| author | Jingxue Bi Yunjia Wang Xin Li Hongji Cao Hongxia Qi Yongkang Wang |
| author_facet | Jingxue Bi Yunjia Wang Xin Li Hongji Cao Hongxia Qi Yongkang Wang |
| author_sort | Jingxue Bi |
| collection | DOAJ |
| description | There are many factors affecting Wi-Fi signal in indoor environment, among which the human body has an important impact. And, its characteristic is related to the user’s orientation. To eliminate positioning errors caused by user’s human body and improve positioning accuracy, this study puts forward an adaptive weighted K -nearest neighbor fingerprint positioning method considering the user’s orientation. First, the orientation fingerprint database model is proposed, which includes the position, orientation, and the sequence of mean received signal strength indicator at each reference point. Second, the fuzzy c -means algorithm is used to cluster orientation fingerprint database taking the hybrid distance of the signal domain and position domain as the clustering feature. Finally, the proposed adaptive algorithm is developed to select K -reference points by matching operation, to remove the reference points with larger signal-domain distances, minimum and maximum coordinate values, and calculate the weighted mean coordinates of the remaining reference points for positioning results. The experimental results show that the average error decreases by 0.7 m, and the root mean square error decreases to about 1.3 m by the proposed technique. And, we conclude that the proposed adaptive weighted K -nearest neighbor fingerprint positioning method can improve positioning accuracy. |
| format | Article |
| id | doaj-art-6061f281c23e43bab2f07fa127a1cd42 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2018-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-6061f281c23e43bab2f07fa127a1cd422025-02-03T06:45:30ZengWileyInternational Journal of Distributed Sensor Networks1550-14772018-06-011410.1177/1550147718785885A novel method of adaptive weighted -nearest neighbor fingerprint indoor positioning considering user’s orientationJingxue Bi0Yunjia Wang1Xin Li2Hongji Cao3Hongxia Qi4Yongkang Wang5School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaThere are many factors affecting Wi-Fi signal in indoor environment, among which the human body has an important impact. And, its characteristic is related to the user’s orientation. To eliminate positioning errors caused by user’s human body and improve positioning accuracy, this study puts forward an adaptive weighted K -nearest neighbor fingerprint positioning method considering the user’s orientation. First, the orientation fingerprint database model is proposed, which includes the position, orientation, and the sequence of mean received signal strength indicator at each reference point. Second, the fuzzy c -means algorithm is used to cluster orientation fingerprint database taking the hybrid distance of the signal domain and position domain as the clustering feature. Finally, the proposed adaptive algorithm is developed to select K -reference points by matching operation, to remove the reference points with larger signal-domain distances, minimum and maximum coordinate values, and calculate the weighted mean coordinates of the remaining reference points for positioning results. The experimental results show that the average error decreases by 0.7 m, and the root mean square error decreases to about 1.3 m by the proposed technique. And, we conclude that the proposed adaptive weighted K -nearest neighbor fingerprint positioning method can improve positioning accuracy.https://doi.org/10.1177/1550147718785885 |
| spellingShingle | Jingxue Bi Yunjia Wang Xin Li Hongji Cao Hongxia Qi Yongkang Wang A novel method of adaptive weighted -nearest neighbor fingerprint indoor positioning considering user’s orientation International Journal of Distributed Sensor Networks |
| title | A novel method of adaptive weighted -nearest neighbor fingerprint indoor positioning considering user’s orientation |
| title_full | A novel method of adaptive weighted -nearest neighbor fingerprint indoor positioning considering user’s orientation |
| title_fullStr | A novel method of adaptive weighted -nearest neighbor fingerprint indoor positioning considering user’s orientation |
| title_full_unstemmed | A novel method of adaptive weighted -nearest neighbor fingerprint indoor positioning considering user’s orientation |
| title_short | A novel method of adaptive weighted -nearest neighbor fingerprint indoor positioning considering user’s orientation |
| title_sort | novel method of adaptive weighted nearest neighbor fingerprint indoor positioning considering user s orientation |
| url | https://doi.org/10.1177/1550147718785885 |
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