  Autonomous Underwater Vehicles (AUVs) commonly utilize an inertial navigation
system (INS) and a Doppler velocity log (DVL) for underwater navigation. To
that end, their measurements are integrated through a nonlinear filter such as
the extended Kalman filter (EKF). The DVL velocity vector estimate depends on
retrieving reflections from the seabed, ensuring that at least three out of its
four transmitted acoustic beams return successfully. When fewer than three
beams are obtained, the DVL cannot provide a velocity update to bind the
navigation solution drift. To cope with this challenge, in this paper, we
propose a hybrid neural coupled (HNC) approach for seamless AUV navigation in
situations of limited DVL measurements. First, we drive an approach to regress
two or three missing DVL beams. Then, those beams, together with the measured
beams, are incorporated into the EKF. We examined INS/DVL fusion both in
loosely and tightly coupled approaches. Our method was trained and evaluated on
recorded data from AUV experiments conducted in the Mediterranean Sea on two
different occasions. The results illustrate that our proposed method
outperforms the baseline loosely and tightly coupled model-based approaches by
an average of 96.15%. It also demonstrates superior performance compared to a
model-based beam estimator by an average of 12.41% in terms of velocity
accuracy for scenarios involving two or three missing beams. Therefore, we
demonstrate that our approach offers seamless AUV navigation in situations of
limited beam measurements.
