Smartphone-Based Indoor Localization With Integrated Fingerprint Signal
Indoor localization of smartphones has received much attention recently and the smartphone localization is essential to a wide range of applications in office buildings, nursing homes, parking lots, and other public places. Existing solutions relying on inertial sensors or received signal strength s...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8999585/ |
| _version_ | 1819295755464081408 |
|---|---|
| author | Peihao Li Xu Yang Yuqing Yin Shouwan Gao Qiang Niu |
| author_facet | Peihao Li Xu Yang Yuqing Yin Shouwan Gao Qiang Niu |
| author_sort | Peihao Li |
| collection | DOAJ |
| description | Indoor localization of smartphones has received much attention recently and the smartphone localization is essential to a wide range of applications in office buildings, nursing homes, parking lots, and other public places. Existing solutions relying on inertial sensors or received signal strength suffer from large location errors and poor stability. We observe an opportunity in the recent trend of increasing numbers of wireless transmitters installed in indoor spaces to design a precise and robust indoor localization solution. We can extract fine-grained channel state information from wireless transmitters for indoor fingerprint localization. However, the accuracy of localization relying on a single physical quantity is limited and difficult to self-correct. This study proposes an integrated channel state information (CSI) and magnetic field strength (MFS) localization method (CSMS) that achieves sub-meter accuracy for smartphones. CSMS constructs an integrated fingerprint map of CSI and MFS and proposes the Local Dynamic Time Warping algorithm for geomagnetic tracking and the Multi-Module Data k-Nearest Neighbor algorithm for fusion fingerprint dynamic weighted comparison. By doing so, CSMS outputs enhanced accuracy with low cost, while overcoming the respective drawbacks of each individual sub-system. We conduct extensive experiments in two scenarios to validate the performance of CSMS. The results of experimental show that the mean distance error in both scenarios is less than 0.5m which is significantly superior to existing smartphone-based indoor positioning methods. |
| first_indexed | 2024-12-24T04:47:15Z |
| format | Article |
| id | doaj.art-b71540f389d64c6d9335b5f787af2a6c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T04:47:15Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b71540f389d64c6d9335b5f787af2a6c2022-12-21T17:14:40ZengIEEEIEEE Access2169-35362020-01-018331783318710.1109/ACCESS.2020.29740388999585Smartphone-Based Indoor Localization With Integrated Fingerprint SignalPeihao Li0https://orcid.org/0000-0002-5576-6543Xu Yang1https://orcid.org/0000-0001-9832-1272Yuqing Yin2Shouwan Gao3https://orcid.org/0000-0003-3379-2350Qiang Niu4School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaIndoor localization of smartphones has received much attention recently and the smartphone localization is essential to a wide range of applications in office buildings, nursing homes, parking lots, and other public places. Existing solutions relying on inertial sensors or received signal strength suffer from large location errors and poor stability. We observe an opportunity in the recent trend of increasing numbers of wireless transmitters installed in indoor spaces to design a precise and robust indoor localization solution. We can extract fine-grained channel state information from wireless transmitters for indoor fingerprint localization. However, the accuracy of localization relying on a single physical quantity is limited and difficult to self-correct. This study proposes an integrated channel state information (CSI) and magnetic field strength (MFS) localization method (CSMS) that achieves sub-meter accuracy for smartphones. CSMS constructs an integrated fingerprint map of CSI and MFS and proposes the Local Dynamic Time Warping algorithm for geomagnetic tracking and the Multi-Module Data k-Nearest Neighbor algorithm for fusion fingerprint dynamic weighted comparison. By doing so, CSMS outputs enhanced accuracy with low cost, while overcoming the respective drawbacks of each individual sub-system. We conduct extensive experiments in two scenarios to validate the performance of CSMS. The results of experimental show that the mean distance error in both scenarios is less than 0.5m which is significantly superior to existing smartphone-based indoor positioning methods.https://ieeexplore.ieee.org/document/8999585/Indoor localizationsmartphonemagnetic fieldschannel state information |
| spellingShingle | Peihao Li Xu Yang Yuqing Yin Shouwan Gao Qiang Niu Smartphone-Based Indoor Localization With Integrated Fingerprint Signal IEEE Access Indoor localization smartphone magnetic fields channel state information |
| title | Smartphone-Based Indoor Localization With Integrated Fingerprint Signal |
| title_full | Smartphone-Based Indoor Localization With Integrated Fingerprint Signal |
| title_fullStr | Smartphone-Based Indoor Localization With Integrated Fingerprint Signal |
| title_full_unstemmed | Smartphone-Based Indoor Localization With Integrated Fingerprint Signal |
| title_short | Smartphone-Based Indoor Localization With Integrated Fingerprint Signal |
| title_sort | smartphone based indoor localization with integrated fingerprint signal |
| topic | Indoor localization smartphone magnetic fields channel state information |
| url | https://ieeexplore.ieee.org/document/8999585/ |
| work_keys_str_mv | AT peihaoli smartphonebasedindoorlocalizationwithintegratedfingerprintsignal AT xuyang smartphonebasedindoorlocalizationwithintegratedfingerprintsignal AT yuqingyin smartphonebasedindoorlocalizationwithintegratedfingerprintsignal AT shouwangao smartphonebasedindoorlocalizationwithintegratedfingerprintsignal AT qiangniu smartphonebasedindoorlocalizationwithintegratedfingerprintsignal |