This work introduces a new method to calculate the water velocity components of a turbulent water column in the x, y, and z directions using Autonomous Underwater Vehicle (AUV) motion response (referred to as the 'WVAM method'). The water column velocities were determined by calculating the difference between the motion responses of the vehicle in calm and turbulent water environments. The velocity components obtained using the WVAM method showed good agreement with measurements from an acoustic Doppler current profiler (ADCP) mounted to the AUV. The standard deviation between the two datasets were below 0.09 m s −1 for the velocity components in the x, y, and z directions, and were within the uncertainty margin of the ADCP measurements. With the WVAM method, it is possible to estimate the velocity components within close proximity to the AUV. This region encompasses the vehicle boundary layer and the ADCP blanking distance, which is not typically resolved. Estimating vertical and horizontal velocities around the boundary layer of the AUV is important for vehicle navigation and control system optimization, and to fill the blanking distance gap within a water column velocity profile, which is important for flow field characterization. The results show that it is possible to estimate the flow field in the vicinity of AUVs and other self-propelled vehicles.

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This work implements a hydrodynamic model-based localization and navigation system for low-cost autonomous underwater vehicles (AUVs) that are limited to a micro-electro mechanical system (MEMS) inertial measurement unit (IMU). The hydrodynamic model of this work is uniquely developed to directly determine the linear velocities of the vehicle using the measured vehicle angular rates and propeller speed as inputs. The proposed system was tested in the field using a fleet of low-cost Bluefin SandShark AUVs. Implementation of the model-based localization system and fusing of the solution into the vehicle navigation loop was conducted using backseat computers of the AUV fleet that run mission orientated operating suite -interval programming (MOOS-IvP). With the model-based navigation system, the maximum localization error (i.e., in comparison to a long baseline (LBL) based ground-truth position) was limited to 15 m and 30 m for two 650-second and 1070-second long missions. Extrapolation of the position drift shows that the model-based localization system is able to limit the position uncertainty to less than 100 m by the end of hour-long mission; whereas, the drift in the default IMU-based localization solution was over 1 km per hour. This is a considerable improvement by only using a MEMS IMU that generally costs less than $100. Furthermore, this work is a step towards generalizing and automating the process of hydrodynamic modeling, model parameter estimation and data fusion (i.e., fusing the localization solution with those from other available aiding sensors and feeding to the navigation loop) so that a model-based localization system can be implemented in any AUV that has backseat computing capability.