  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 coarsereceived signal strength (RSS). However,
the acquisition of CSI requires the cooperation of access points (APs) and
involves only dataframes, which imposes restrictions on real-world deployment.
In this paper, we present CRISLoc, the first CSI fingerprinting
basedlocalization prototype system using ubiquitous smartphones. CRISLoc
operates in a completely passive mode, overhearing thepackets on-the-fly for
his own CSI acquisition. The smartphone CSI is sanitized via calibrating the
distortion enforced by WiFi amplifiercircuits. CRISLoc tackles the challenge of
altered APs with a joint clustering and outlier detection method to find them.
A novel transferlearning approach is proposed to reconstruct the
high-dimensional CSI fingerprint database on the basis of the outdated
fingerprintsand a few fresh measurements, and an enhanced KNN approach is
proposed to pinpoint the location of a smartphone. Our studyreveals important
properties about the stability and sensitivity of smartphone CSI that has not
been reported previously. Experimentalresults show that CRISLoc can achieve a
mean error of around 0.29m in a6m times 8mresearch laboratory. The mean error
increases by 5.4 cm and 8.6 cm upon the movement of one and two APs, which
validates the robustness of CRISLoc against environment changes.
