This article presents a modeling method for the uncorrelated measurement error of the ultra-short baseline (USBL) acoustic positioning system for aiding navigation of underwater vehicles. The Mahalanobis distance (MD) and principal component analysis are applied to decorrelate the errors of USBL measurements, which are correlated in the x- and y-directions and vary according to the relative direction and distance between a reference station and the underwater vehicles. The proposed method can decouple the radial-direction error and angular direction error from each USBL measurement, where the former and latter are independent and dependent, respectively, of the distance between the reference station and the vehicle. With the decorrelation of the USBL errors along the trajectory of the vehicles in every time step, the proposed method can reduce the threshold of the outlier decision level. To demonstrate the effectiveness of the proposed method, simulation studies were performed with motion data obtained from a field experiment involving an autonomous underwater vehicle and USBL signals generated numerically by matching the specifications of a specific USBL with the data of a global positioning system. The simulations indicated that the navigation system is more robust in rejecting outliers of the USBL measurements than conventional ones. In addition, it was shown that the erroneous estimation of the navigation system after a long USBL blackout can converge to the true states using the MD of the USBL measurements. The navigation systems using the uncorrelated error model of the USBL, therefore, can effectively eliminate USBL outliers without loss of uncontaminated signals.

This study presents a pressure variation model (PVM) derived from the regression analysis of dynamic pressure computed through numerical analysis to estimate the velocity of underwater vehicles. Furthermore, the drift angle estimation algorithm was developed using predicted velocities from PVM and pressure sensor differences. This approach estimates the single-motion states of underwater vehicles, such as straight, turning, and gliding. Furthermore, it confirms the viability of state estimation even in multiple motions involving turning and gliding motion with a drift angle and spiral motion. The comparison with numerical analysis results validated prediction accuracy within 15%.

Autonomous underwater vehicles have long been used in marine explorations, and their application in recent polar expeditions is particularly noteworthy. However, the complexity and extreme conditions of the polar environment pose risks to the stable operation of autonomous underwater vehicles. This study adopted the methodology of fuzzy fault tree analysis to deeply analyze the operational risks of autonomous underwater vehicles in polar environments. While traditional fault tree analysis maps the causal relationships and probabilities between basic and intermediate events, fuzzy fault tree analysis models the uncertainty of data and determines the failure probability by integrating expert opinions. This study revealed that polar environment-induced failures play a more substantial role in autonomous underwater vehicle loss in polar regions than inherent system failures. The study identified ‘recovery failure’ and ‘poor communication’ as the major risk factors facing autonomous underwater vehicles in polar environments, exhibiting the highest failure probabilities. Specifically, among various polar environmental factors, ‘large ice concentration’, ‘ice thickness’, and ‘roughness of ice underside’ under ‘bad’ conditions were found to have a significant impact on the autonomous underwater vehicle’s failure probability. The fuzzy fault tree analysis method in this study successfully filled the gap created by the absence of historical data by effectively incorporating expert opinions, enabling a quantitative presentation of the impact of polar environments, which has been previously difficult to convey in qualitative terms.

This study investigates the transient behavior of an electro-thermal drilling probe (ETDP) during a close-contact melting process within a glacier. In particular, the present work analyzes the effect of the tip temperature on the formation of molten thin liquid films and the subsequent rate of penetration (ROP) through numerical simulation. We used the commercial code of ANSYS Fluent (v.17.2) to solve the Reynolds-averaged Navier–Stokes equation, together with an energy equation considering the solidification and melting model. The ROP of the drilling probe is determined based on the energy balance between the heating power and melting rate of ice. As the results, the ETDP penetrates the ice through a close-contact melting process. The molten liquid layer with less than 1 mm of thickness forms near the heated probe tip. In addition, the ROP increases with the heated temperature of the probe tip.