The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 375–380, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-375-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 375–380, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-375-2020

  25 Aug 2020

25 Aug 2020

COLLABORATIVE Wi-Fi FINGERPRINTING INDOOR POSITIONING USING NEAR RELATION

Y. Wang, W. Wang, X. Li, W. Zhang, and R. Guo Y. Wang et al.
  • 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, Shenzhen, P.R. China

Keywords: Indoor Positioning, Near relation, Sparse Wi-Fi Fingerprint, Fingerprint Ambiguity

Abstract. Indoor positioning is of great importance to the era of mobile computing. Currently, much attention has been paid to RSS-based location for that it can provide position information without additional equipment. However, this method suffers from many challenges: (1) fingerprint ambiguity; (2) labor-intensive of fingerprint collection; (3) low efficiency of fingerprint matching. To get over these drawbacks, we provide a collaborative WiFi fingerprinting indoor positioning method using near relation. The base idea of this method is that interpolation method is used to enrich sparse Wi-Fi fingerprint. Near relation boundary is provided and Wi-Fi fingerprints is constrained to this region to reduce fingerprint ambiguity, which also can improve the efficiency of fingerprint matching. Extensive experiments show that a positioning accuracy of 3.8 m can be achieved with the near relation under 1 m interpolation density.