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10.3390/ijgi9120714
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Article

Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints

by
Yankun Wang
1,2,
Renzhong Guo
1,
Weixi Wang
1,
Xiaoming Li
1,*,
Shengjun Tang
1,
Wei Zhang
1,
Luyao Wang
2,
Liang Chen
2,
You Li
1 and
Wenqun Xiu
3
1
National Engineering Laboratory for Big Data System Computing Technology & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University& Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518052, China
2
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
3
Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518000, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(12), 714; https://doi.org/10.3390/ijgi9120714
Submission received: 11 September 2020 / Revised: 17 October 2020 / Accepted: 30 November 2020 / Published: 1 December 2020
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
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Abstract

Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint collection. To overcome these drawbacks, we provide a near relation-based indoor positioning method under a sparse Wi-Fi fingerprint. To effectively obtain the fingerprint database, certain interpolation methods are used to enrich sparse Wi-Fi fingerprints. A near relation boundary is provided, and Wi-Fi fingerprints are constrained to this region to reduce fingerprint ambiguity, which can also improve the efficiency of fingerprint matching. Extensive experiments show that the kriging interpolation method performs well, and a positioning accuracy of 2.86 m can be achieved with a near relation under a 1 m interpolation density.
Keywords: indoor positioning; near relation; sparse Wi-Fi fingerprint; fingerprint ambiguity indoor positioning; near relation; sparse Wi-Fi fingerprint; fingerprint ambiguity

Share and Cite

MDPI and ACS Style

Wang, Y.; Guo, R.; Wang, W.; Li, X.; Tang, S.; Zhang, W.; Wang, L.; Chen, L.; Li, Y.; Xiu, W. Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints. ISPRS Int. J. Geo-Inf. 2020, 9, 714. https://doi.org/10.3390/ijgi9120714

AMA Style

Wang Y, Guo R, Wang W, Li X, Tang S, Zhang W, Wang L, Chen L, Li Y, Xiu W. Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints. ISPRS International Journal of Geo-Information. 2020; 9(12):714. https://doi.org/10.3390/ijgi9120714

Chicago/Turabian Style

Wang, Yankun, Renzhong Guo, Weixi Wang, Xiaoming Li, Shengjun Tang, Wei Zhang, Luyao Wang, Liang Chen, You Li, and Wenqun Xiu. 2020. "Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints" ISPRS International Journal of Geo-Information 9, no. 12: 714. https://doi.org/10.3390/ijgi9120714

APA Style

Wang, Y., Guo, R., Wang, W., Li, X., Tang, S., Zhang, W., Wang, L., Chen, L., Li, Y., & Xiu, W. (2020). Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints. ISPRS International Journal of Geo-Information, 9(12), 714. https://doi.org/10.3390/ijgi9120714

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