Gain Without Pain: Accurate WiFi-based Localization using Fingerprint Spatial Gradient

Among numerous indoor localization systems proposed during the past decades, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. However, current WiFi fingerprinting suffers from a pivotal problem of RSS fluctuations caused by unpredictable environmental dynamics. The RSS variations lead to severe spatial ambiguity and temporal instability in RSS fingerprinting, both impairing the location accuracy. To overcome such drawbacks, we propose fingerprint spatial gradient (FSG), a more stable and distinctive form than RSS fingerprints, which exploits the spatial relationships among the RSS fingerprints of multiple neighbouring locations. As a spatially relative form, FSG is more resistant to RSS uncertainties. Based on the concept of FSG, we design novel algorithms to construct FSG on top of a general RSS fingerprint database and then propose effective FSG matching methods for location estimation. Unlike previous works, the resulting system, named ViVi, yields performance gain without the pains of introducing extra information or additional service restrictions or assuming impractical RSS models. Extensive experiments in different buildings demonstrate that ViVi achieves great performance, outperforming the best among four comparative start-of-the-art approaches by 29% in mean accuracy and 19% in 95th percentile accuracy and outweighing the worst one by 39% and 24% respectively. We envision FSG as a promising supplement and alternative to existing RSS fingerprinting for future WiFi localization.