  Recently, round-trip time (RTT) measured by a fine-timing measurement
protocol has received great attention in the area of WiFi positioning. It
provides an acceptable ranging accuracy in favorable environments when a
line-of-sight (LOS) path exists. Otherwise, a signal is detoured along with
non-LOS paths, making the resultant ranging results different from the
ground-truth, called an RTT bias, which is the main reason for poor positioning
performance. To address it, we aim at leveraging the user mobility trajectory
detected by a smartphone's inertial measurement units, called pedestrian dead
reckoning (PDR). Specifically, PDR provides the geographic relation among
adjacent locations, guiding the resultant positioning estimates' sequence not
to deviate from the user trajectory. To this end, we describe their relations
as multiple geometric equations, enabling us to render a novel positioning
algorithm with acceptable accuracy. Depending on the mobility pattern being
linear or arbitrary, we develop different algorithms divided into two phases.
First, we can jointly estimate an RTT bias of each AP and the user's step
length by leveraging the geometric relation mentioned above. It enables us to
construct a user's relative trajectory defined on the concerned AP's local
coordinate system. Second, we align every AP's relative trajectory into a
single one, called trajectory alignment, equivalent to transformation to the
global coordinate system. As a result, we can estimate the sequence of the
user's absolute locations from the aligned trajectory. Various field
experiments extensively verify the proposed algorithm's effectiveness that the
average positioning error is approximately 0.369 (m) and 1.705 (m) in LOS and
NLOS environments, respectively.
