  Because each indoor site has its own radio propagation characteristics, a
site survey process is essential to optimize a Wi-Fi ranging strategy for
range-based positioning solutions. This paper studies an unsupervised learning
technique that autonomously investigates the characteristics of the surrounding
environment using sensor data accumulated while users use a positioning
application. Using the collected sensor data, the device trajectory can be
regenerated, and a Wi-Fi ranging module is trained to make the shape of the
estimated trajectory using Wi-Fi similar to that obtained from sensors. In this
process, the ranging module learns the way to identify the channel conditions
from each Wi-Fi access point (AP) and produce ranging results accordingly.
Furthermore, we collect the channel state information (CSI) from beacon frames
and evaluate the benefit of using CSI in addition to received signal strength
(RSS) measurements. When CSI is available, the ranging module can identify more
diverse channel conditions from each AP, and thus more precise positioning
results can be achieved. The effectiveness of the proposed learning technique
is verified using a real-time positioning application implemented on a PC
platform.
