CRISLoc: Reconstructable CSI Fingerprinting for Indoor Smartphone Localization

Abstract

Channel-state information (CSI)-based fingerprinting for WIFI indoor localization has attracted lots of attention very recently. The frequency diverse and temporally stable CSI better represents the location-dependent channel characteristics than the coarse received signal strength (RSS). However, the acquisition of CSI requires the cooperation of access points (APs) and involves only data frames, which imposes restrictions on real-world deployment. In this article, we present CRISLoc, the first CSI fingerprinting-based localization prototype system using ubiquitous smartphones. CRISLoc operates in a completely passive mode, overhearing the packets on-the-fly for his own CSI acquisition. The smartphone CSI is sanitized via calibrating the distortion enforced by WiFi amplifier circuits. CRISLoc tackles the challenge of altered APs with a joint clustering and outlier detection method to find them. A novel transfer learning approach is proposed to reconstruct the high-dimensional CSI fingerprint database on the basis of the outdated fingerprints and a few fresh measurements, and an enhanced KNN approach is proposed to pinpoint the location of a smartphone. Our study reveals important properties about the stability and sensitivity of smartphone CSI that has not been reported previously. Experimental results show that CRISLoc can achieve a mean error of around 0.29 m in a 6 m $\times$ 8 m research laboratory. The mean error increases by 5.4 and 8.6 cm upon the movement of one and two APs, which validates the robustness of CRISLoc against environmental changes.