Towards Accurate Smartphone Localization Using CSI Measurements

Abstract

Abstract In comparison with capturing channel state information (CSI) measurements via a laptop or desktop, using a smartphone to collect CSI measurements incurs the restriction of working with a single access point and significant signal distortions, resulting in limited information for smartphone localization. Therefore, this paper intends to leverage as much available localization information as possible by ($1$) shifting the WiFi frequency from $2.4$ to $5$GHz; ($2$) calibrating the noisy CSI measurements and ($3$) fusing both amplitudes and phases of the CSI measurements, so as to enhance localization accuracy. Specifically, we first filter out distorted CSI measurements based on their distribution characteristics, then apply the advanced uniform manifold approximation and projection method to refine the mapping relations from a high-dimensional fingerprint space to a low-dimensional location space, and design a location fusion algorithm based on the continuous feature scaling model, which is able to distinguish two locations with similar fingerprints. Extensive experimental results show that the localization accuracy of the proposed approach outperforms the state-of-the-art counterparts by at least $15.5$ and $18.7\%$ using two off-the-shelf smartphones.