COLLABORATIVE Wi-Fi FINGERPRINTING INDOOR POSITIONING USING NEAR RELATION
- 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.