  Accurate alignment of a fixed mobile device equipped with inertial sensors
inside a moving vehicle is important for navigation, activity recognition, and
other applications. Accurate estimation of the device mounting angle is
required to rotate the inertial measurement from the sensor frame to the moving
platform frame to standardize measurements and improve the performance of the
target task. In this work, a data-driven approach using deep neural networks
(DNNs) is proposed to learn the yaw mounting angle of a smartphone equipped
with an inertial measurement unit (IMU) and strapped to a car. The proposed
model uses only the accelerometer and gyroscope readings from an IMU as input
and, in contrast to existing solutions, does not require global position inputs
from global navigation satellite systems (GNSS). To train the model in a
supervised manner, IMU data is collected for training and validation with the
sensor mounted at a known yaw mounting angle, and a range of ground truth
labels is generated by applying a random rotation in a bounded range to the
measurements. The trained model is tested on data with real rotations showing
similar performance as with synthetic rotations. The trained model is deployed
on an Android device and evaluated in real-time to test the accuracy of the
estimated yaw mounting angle. The model is shown to find the mounting angle at
an accuracy of 8 degrees within 5 seconds, and 4 degrees within 27 seconds. An
experiment is conducted to compare the proposed model with an existing
off-the-shelf solution.
