Optimal estimation of glider's underwater trajectory with depth-dependent correction using the Navy Coastal Ocean Model with application to antisubmarine warfare

An underwater glider is a cost-effective underwater unmanned vehicle with high-endurance for oceanographic research or naval applications. Its navigation and localization accuracy are important because these accuracies provide spatiotemporally high resolution ocean data with saving energy and time. The glider, however, is affected by the ocean currents because of its minimal velocity, which is due to its buoyancy-driven propulsion system. It also lacks of inexpensive and efficient localization sensors during its subsurface mission. Therefore, knowing its precise underwater position is a challenging task. This study attempts to develop a novel correction method for estimating a glider’s optimal underwater trajectory. In four steps, it compares the corrected trajectories, which are developed using depth-averaged and depth-dependent correction methods using the Regional Navy Coastal Ocean Model (NCOM). The results suggest that the depth-dependent correction method is more accurate. This study for estimating a glider’s underwater trajectory accurately would be beneficial to oceanographic research and naval applications, especially antisubmarine warfare (ASW) such as operating Intelligence, Surveillance, and Reconnaissance (ISR); operating littoral ASW; providing communication networks; and supporting tactical oceanography.http://archive.org/details/optimalestimatio1094544002Outstanding ThesisLieutenant Commander, Republic of Korea NavyApproved for public release; distribution is unlimited

Deep-Sea Model-Aided Navigation Accuracy for Autonomous Underwater Vehicles Using Online Calibrated Dynamic Models

In this work, the accuracy of inertial-based navigation systems for autonomous underwater vehicles (AUVs) in typical mapping and exploration missions up to 5000m depth is examined. The benefit of using an additional AUV motion model in the navigation is surveyed. Underwater navigation requires acoustic positioning sensors. In this work, so-called Ultra-Short-Baseline (USBL) devices were used allowing the AUV to localize itself relative to an opposite device attached to a (surface) vehicle. Despite their easy use, the devices\u27 absolute positioning accuracy decreases proportional to range. This makes underwater navigation a sophisticated estimation task requiring integration of multiple sensors for inertial, orientation, velocity and position measurements. First, error models for the necessary sensors are derived. The emphasis is on the USBL devices due to their key role in navigation - besides a velocity sensor based on the Doppler effect. The USBL model is based on theoretical considerations and conclusions from experimental data. The error models and the navigation algorithms are evaluated on real-world data collected during field experiments in shallow sea. The results of this evaluation are used to parametrize an AUV motion model. Usually, such a model is used only for model-based motion control and planning. In this work, however, besides serving as a simulation reference model, it is used as a tool to improve navigation accuracy by providing virtual measurements to the navigation algorithm (model-aided navigation). The benefit of model-aided navigation is evaluated through Monte Carlo simulation in a deep-sea exploration mission. The final and main contributions of this work are twofold. First, the basic expected navigation accuracy for a typical deep-sea mission with USBL and an ensemble of high-quality navigation sensors is evaluated. Secondly, the same setting is examined using model-aided navigation. The model-aiding is activated after the AUV gets close to sea-bottom. This reflects the case where the motion model is identified online which is only feasible if the velocity sensor is close to the ground (e.g. 100m or closer). The results indicate that, ideally, deep-sea navigation via USBL can be achieved with an accuracy in range of 3-15m w.r.t. the expected root-mean-square error. This also depends on the reference vehicle\u27s position at the surface. In case the actual estimation certainty is already below a certain threshold (ca. <4m), the simulations reveal that the model-aided scheme can improve the navigation accuracy w.r.t. position by 3-12%