  Inertial Measurement Unit (IMU) has long been a dream for stable and reliable
motion estimation, especially in indoor environments where GPS strength limits.
In this paper, we propose a novel method for position and orientation
estimation of a moving object only from a sequence of IMU signals collected
from the phone. Our main observation is that human motion is monotonous and
periodic. We adopt the Extended Kalman Filter and use the learning-based method
to dynamically update the measurement noise of the filter. Our pedestrian
motion tracking system intends to accurately estimate planar position,
velocity, heading direction without restricting the phone's daily use. The
method is not only tested on the self-collected signals, but also provides
accurate position and velocity estimations on the public RIDI dataset, i.e.,
the absolute transmit error is 1.28m for a 59-second sequence.
