  The fine timing measurement (FTM) protocol is designed to determine precise
ranging between Wi-Fi devices using round-trip time (RTT) measurements.
However, the multipath propagation of radio waves generates inaccurate timing
information, degrading the ranging performance. In this study, we use a neural
network (NN) to adaptively learn the unique measurement patterns observed at
different indoor environments and produce enhanced ranging outputs from raw FTM
measurements. Moreover, the NN is trained based on an unsupervised learning
framework, using the naturally accumulated sensor data acquired from users
accessing location services. Therefore, the effort involved in collecting
training data is significantly minimized. The experimental results verified
that the collection of unlabeled data for a short duration is sufficient to
learn the pattern in raw FTM measurements and produce improved ranging results.
The proposed method reduced the ranging errors in raw distance measurements and
well-calibrated ranging results requiring the collection of ground truth data
by 47-50% and 17-29%, respectively. Consequently, positioning error reduced by
17-30% compared to the result with well-calibrated ranging.
