  The fusion between an inertial navigation system and global navigation
satellite systems is regularly used in many platforms such as drones, land
vehicles, and marine vessels. The fusion is commonly carried out in a
model-based extended Kalman filter framework. One of the critical parameters of
the filter is the process noise covariance. It is responsible for the real-time
solution accuracy, as it considers both vehicle dynamics uncertainty and the
inertial sensors quality. In most situations, the process noise is covariance
assumed to be constant. Yet, due to vehicle dynamics and sensor measurement
variations throughout the trajectory, the process noise covariance is subject
to change. To cope with such situations, several adaptive model-based Kalman
filters were suggested in the literature. In this paper, we propose a hybrid
model and learning-based adaptive navigation filter. We rely on the model-based
Kalman filter and design a deep neural network model to tune the momentary
system noise covariance matrix, based only on the inertial sensor readings.
Once the process noise covariance is learned, it is plugged into the
well-established, model-based Kalman filter. After deriving the proposed hybrid
framework, field experiment results using a quadrotor are presented and a
comparison to model-based adaptive approaches is given. We show that the
proposed method obtained an improvement of 25% in the position error.
Furthermore, the proposed hybrid learning method can be used in any navigation
filter and also in any relevant estimation problem.
