Fingerprint-based Wi-Fi indoor localization using map and inertial sensors
It is a common understanding that the localization accuracy can be improved by indoor maps and inertial sensors. However, there is a lack of concrete and generic solutions that combine these two features together and practically demonstrate its validity. This article aims to provide such a solution...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717749817 |
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| _version_ | 1850168625674059776 |
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| author | Xingwang Wang Xiaohui Wei Yuanyuan Liu Kun Yang Xuan Du |
| author_facet | Xingwang Wang Xiaohui Wei Yuanyuan Liu Kun Yang Xuan Du |
| author_sort | Xingwang Wang |
| collection | DOAJ |
| description | It is a common understanding that the localization accuracy can be improved by indoor maps and inertial sensors. However, there is a lack of concrete and generic solutions that combine these two features together and practically demonstrate its validity. This article aims to provide such a solution based on the mainstream fingerprint-based indoor localization approach. First, we introduce the theorem called reference points placement , which gives a theoretical guide to place reference points. Second, we design a Wi-Fi signal propagation-based cluster algorithm to reduce the amount of computation. The paper gives a parameter called reliability to overcome the skewing of inertial sensors. Then we also present Kalman filter and Markov chain to predict the system status. The system is able to provide high-accuracy real-time tracking by integrating indoor map and inertial sensors with Wi-Fi signal strength. Finally, the proposed work is evaluated and compared with the previous Wi-Fi indoor localization systems. In addition, the effect of inertial sensors’ reliability is also discussed. Results are drawn from a campus office building which is about 80 m×140 m with 57 access points. |
| format | Article |
| id | doaj-art-a23160bf441a4cbdbd9133efa5014c94 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-a23160bf441a4cbdbd9133efa5014c942025-08-20T02:20:55ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-12-011310.1177/1550147717749817Fingerprint-based Wi-Fi indoor localization using map and inertial sensorsXingwang Wang0Xiaohui Wei1Yuanyuan Liu2Kun Yang3Xuan Du4College of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaSchool of Computer Science and Electronic Engineering, University of Essex, Colchester UKSchool of Computer Science and Electronic Engineering, University of Essex, Colchester UKIt is a common understanding that the localization accuracy can be improved by indoor maps and inertial sensors. However, there is a lack of concrete and generic solutions that combine these two features together and practically demonstrate its validity. This article aims to provide such a solution based on the mainstream fingerprint-based indoor localization approach. First, we introduce the theorem called reference points placement , which gives a theoretical guide to place reference points. Second, we design a Wi-Fi signal propagation-based cluster algorithm to reduce the amount of computation. The paper gives a parameter called reliability to overcome the skewing of inertial sensors. Then we also present Kalman filter and Markov chain to predict the system status. The system is able to provide high-accuracy real-time tracking by integrating indoor map and inertial sensors with Wi-Fi signal strength. Finally, the proposed work is evaluated and compared with the previous Wi-Fi indoor localization systems. In addition, the effect of inertial sensors’ reliability is also discussed. Results are drawn from a campus office building which is about 80 m×140 m with 57 access points.https://doi.org/10.1177/1550147717749817 |
| spellingShingle | Xingwang Wang Xiaohui Wei Yuanyuan Liu Kun Yang Xuan Du Fingerprint-based Wi-Fi indoor localization using map and inertial sensors International Journal of Distributed Sensor Networks |
| title | Fingerprint-based Wi-Fi indoor localization using map and inertial sensors |
| title_full | Fingerprint-based Wi-Fi indoor localization using map and inertial sensors |
| title_fullStr | Fingerprint-based Wi-Fi indoor localization using map and inertial sensors |
| title_full_unstemmed | Fingerprint-based Wi-Fi indoor localization using map and inertial sensors |
| title_short | Fingerprint-based Wi-Fi indoor localization using map and inertial sensors |
| title_sort | fingerprint based wi fi indoor localization using map and inertial sensors |
| url | https://doi.org/10.1177/1550147717749817 |
| work_keys_str_mv | AT xingwangwang fingerprintbasedwifiindoorlocalizationusingmapandinertialsensors AT xiaohuiwei fingerprintbasedwifiindoorlocalizationusingmapandinertialsensors AT yuanyuanliu fingerprintbasedwifiindoorlocalizationusingmapandinertialsensors AT kunyang fingerprintbasedwifiindoorlocalizationusingmapandinertialsensors AT xuandu fingerprintbasedwifiindoorlocalizationusingmapandinertialsensors |