  A deep neural network (DNN) is trained to estimate the speed of a car driving
in an urban area using as input a stream of measurements from a low-cost
six-axis inertial measurement unit (IMU). Three hours of data was collected by
driving through the city of Ashdod, Israel in a car equipped with a global
navigation satellite system (GNSS) real time kinematic (RTK) positioning device
and a synchronized IMU. Ground truth labels for the car speed were calculated
using the position measurements obtained at the high rate of 50 Hz. A DNN
architecture with long short-term memory layers is proposed to enable
high-frequency speed estimation that accounts for previous inputs history and
the nonlinear relation between speed, acceleration and angular velocity. A
simplified aided dead reckoning localization scheme is formulated to assess the
trained model which provides the speed pseudo-measurement. The trained model is
shown to substantially improve the position accuracy during a 4 minutes drive
without the use of GNSS position updates.
