  One of the primary means of sea exploration is autonomous underwater vehicles
(AUVs). To perform these tasks, AUVs must navigate the rough challenging sea
environment. AUVs usually employ an inertial navigation system (INS), aided by
a Doppler velocity log (DVL), to provide the required navigation accuracy. The
DVL transmits four acoustic beams to the seafloor, and by measuring changes in
the frequency of the returning beams, the DVL can estimate the AUV velocity
vector. However, in practical scenarios, not all the beams are successfully
reflected. When only three beams are available, the accuracy of the velocity
vector is degraded. When fewer than three beams are reflected, the DVL cannot
estimate the AUV velocity vector. This paper presents a data-driven approach,
MissBeamNet, to regress the missing beams in partial DVL beam measurement
cases. To that end, a deep neural network (DNN) model is designed to process
the available beams along with past DVL measurements to regress the missing
beams. The AUV velocity vector is estimated using the available measured and
regressed beams. To validate the proposed approach, sea experiments were made
with the "Snapir" AUV, resulting in an 11 hours dataset of DVL measurements.
Our results show that the proposed system can accurately estimate velocity
vectors in situations of missing beam measurements. Our dataset and codebase
implementing the described framework is available at our GitHub repository
https://github.com/ansfl/MissBeamNet .
