  This paper presents a deep learning approach to aid dead-reckoning (DR)
navigation using a limited sensor suite. A Recurrent Neural Network (RNN) was
developed to predict the relative horizontal velocities of an Autonomous
Underwater Vehicle (AUV) using data from an IMU, pressure sensor, and control
inputs. The RNN network is trained using experimental data, where a doppler
velocity logger (DVL) provided ground truth velocities. The predictions of the
relative velocities were implemented in a dead-reckoning algorithm to
approximate north and east positions. The studies in this paper were twofold I)
Experimental data from a Long-Range AUV was investigated. Datasets from a
series of surveys in Monterey Bay, California (U.S) were used to train and test
the RNN network. II) The second study explore datasets generated by a simulated
autonomous underwater glider. Environmental variables e.g ocean currents were
implemented in the simulation to reflect real ocean conditions. The proposed
neural network approach to DR navigation was compared to the on-board
navigation system and ground truth simulated positions.
