Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System
Recently, as wireless infrastructure has developed widely, and smartphones have become necessary in daily life, indoor positioning devices and applications have become more and more popular. Previous studies have proposed several methods based on different wireless communication technologies. Among...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-05-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.9746/jcmsi.12.109 |
| _version_ | 1797661014561390592 |
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| author | Shigeyuki Tateno Tong Li Yu Wu Ziyuan Wang |
| author_facet | Shigeyuki Tateno Tong Li Yu Wu Ziyuan Wang |
| author_sort | Shigeyuki Tateno |
| collection | DOAJ |
| description | Recently, as wireless infrastructure has developed widely, and smartphones have become necessary in daily life, indoor positioning devices and applications have become more and more popular. Previous studies have proposed several methods based on different wireless communication technologies. Among them, methods with received signal strength indicator (RSSI) values and trilateration methods are mainly used to obtain positioning results. However, due to abnormal RSSI values caused by noise and influence from the environment, the accuracies of these methods are not satisfying. Therefore, a new method which can reduce the positioning error is necessary. In this paper, to improve the positioning accuracy above trilateration results, an access point selection method and a kernel density estimation method are combined to obtain estimated points. Experiments are designed in actual environments, and the results of which show that the proposed method is sufficient for improving the positioning accuracy. |
| first_indexed | 2024-03-11T18:39:15Z |
| format | Article |
| id | doaj.art-e35768b00a5d44689983360feb1884cb |
| institution | Directory Open Access Journal |
| issn | 1884-9970 |
| language | English |
| last_indexed | 2024-03-11T18:39:15Z |
| publishDate | 2019-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj.art-e35768b00a5d44689983360feb1884cb2023-10-12T13:43:55ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702019-05-0112310911510.9746/jcmsi.12.10912103259Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning SystemShigeyuki Tateno0Tong Li1Yu Wu2Ziyuan Wang3Graduate School of Information, Production and Systems, Waseda UniversityGraduate School of Information, Production and Systems, Waseda UniversityGraduate School of Information, Production and Systems, Waseda UniversityGraduate School of Information, Production and Systems, Waseda UniversityRecently, as wireless infrastructure has developed widely, and smartphones have become necessary in daily life, indoor positioning devices and applications have become more and more popular. Previous studies have proposed several methods based on different wireless communication technologies. Among them, methods with received signal strength indicator (RSSI) values and trilateration methods are mainly used to obtain positioning results. However, due to abnormal RSSI values caused by noise and influence from the environment, the accuracies of these methods are not satisfying. Therefore, a new method which can reduce the positioning error is necessary. In this paper, to improve the positioning accuracy above trilateration results, an access point selection method and a kernel density estimation method are combined to obtain estimated points. Experiments are designed in actual environments, and the results of which show that the proposed method is sufficient for improving the positioning accuracy.http://dx.doi.org/10.9746/jcmsi.12.109received signal strength indicatoraccess point selectionkernel density estimation |
| spellingShingle | Shigeyuki Tateno Tong Li Yu Wu Ziyuan Wang Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System SICE Journal of Control, Measurement, and System Integration received signal strength indicator access point selection kernel density estimation |
| title | Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System |
| title_full | Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System |
| title_fullStr | Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System |
| title_full_unstemmed | Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System |
| title_short | Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System |
| title_sort | combination of statistical access point selection methods based on rssi in indoor positioning system |
| topic | received signal strength indicator access point selection kernel density estimation |
| url | http://dx.doi.org/10.9746/jcmsi.12.109 |
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