Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor Environment
Indoor positioning in a multi-floor environment by using a smartphone is considered in this paper. The positioning accuracy and robustness of WiFi fingerprinting-based positioning are limited due to the unexpected variation of WiFi measurements between floors. On this basis, we propose a novel smart...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-03-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-666X/6/3/347 |
| _version_ | 1811279956757446656 |
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| author | Zengshan Tian Xin Fang Mu Zhou Lingxia Li |
| author_facet | Zengshan Tian Xin Fang Mu Zhou Lingxia Li |
| author_sort | Zengshan Tian |
| collection | DOAJ |
| description | Indoor positioning in a multi-floor environment by using a smartphone is considered in this paper. The positioning accuracy and robustness of WiFi fingerprinting-based positioning are limited due to the unexpected variation of WiFi measurements between floors. On this basis, we propose a novel smartphone-based integrated WiFi/MEMS positioning algorithm based on the robust extended Kalman filter (EKF). The proposed algorithm first relies on the gait detection approach and quaternion algorithm to estimate the velocity and heading angles of the target. Second, the velocity and heading angles, together with the results of WiFi fingerprinting-based positioning, are considered as the input of the robust EKF for the sake of conducting two-dimensional (2D) positioning. Third, the proposed algorithm calculates the height of the target by using the real-time recorded barometer and geographic data. Finally, the experimental results show that the proposed algorithm achieves the positioning accuracy with root mean square errors (RMSEs) less than 1 m in an actual multi-floor environment. |
| first_indexed | 2024-04-13T01:05:04Z |
| format | Article |
| id | doaj.art-19b4dbf42d09461cbf78c35dee64bc00 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-04-13T01:05:04Z |
| publishDate | 2015-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-19b4dbf42d09461cbf78c35dee64bc002022-12-22T03:09:21ZengMDPI AGMicromachines2072-666X2015-03-016334736310.3390/mi6030347mi6030347Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor EnvironmentZengshan Tian0Xin Fang1Mu Zhou2Lingxia Li3Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaChongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaChongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaChongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaIndoor positioning in a multi-floor environment by using a smartphone is considered in this paper. The positioning accuracy and robustness of WiFi fingerprinting-based positioning are limited due to the unexpected variation of WiFi measurements between floors. On this basis, we propose a novel smartphone-based integrated WiFi/MEMS positioning algorithm based on the robust extended Kalman filter (EKF). The proposed algorithm first relies on the gait detection approach and quaternion algorithm to estimate the velocity and heading angles of the target. Second, the velocity and heading angles, together with the results of WiFi fingerprinting-based positioning, are considered as the input of the robust EKF for the sake of conducting two-dimensional (2D) positioning. Third, the proposed algorithm calculates the height of the target by using the real-time recorded barometer and geographic data. Finally, the experimental results show that the proposed algorithm achieves the positioning accuracy with root mean square errors (RMSEs) less than 1 m in an actual multi-floor environment.http://www.mdpi.com/2072-666X/6/3/347multi-floor positioningWiFi fingerprintingMEMSextended Kalman filter |
| spellingShingle | Zengshan Tian Xin Fang Mu Zhou Lingxia Li Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor Environment Micromachines multi-floor positioning WiFi fingerprinting MEMS extended Kalman filter |
| title | Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor Environment |
| title_full | Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor Environment |
| title_fullStr | Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor Environment |
| title_full_unstemmed | Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor Environment |
| title_short | Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor Environment |
| title_sort | smartphone based indoor integrated wifi mems positioning algorithm in a multi floor environment |
| topic | multi-floor positioning WiFi fingerprinting MEMS extended Kalman filter |
| url | http://www.mdpi.com/2072-666X/6/3/347 |
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