Underwater Navigation of AUVs Using Uncorrelated Measurement Error Model of USBL

Abstract

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.

Estimation of Underwater Workable Time Considering Tidal Currents: A Review of Diving Equipment

“…In addition, managers with specialist maritime knowledge that can perform these tasks must be trained. Such marine environment prediction information (Kim and Lee, 2022;Park et al, 2021) can be applied to the planning and operation of Remotely Operated Vehicles (ROVs), Unmanned Underwater Vehicles (UUVs), and Autonomous Underwater Vehicles (AUVs), which do not require divers (Jang, Do, and Kim, 2022;Kang et al, 2022;Kim et al, 2021;Kim et al, 2020;Lee et al, 2022).…”

Voronoi Diagram-based USBL Outlier Rejection for AUV Localization

“…Using data Previous studies on removing USBL outliers include using a median filter to calculate the median, measuring the distance between the median and current data, and considering it an outlier if it exceeds a set threshold (Morgada et al, 2015). Another study modeled the measurement errors and removed outliers based on the Mahalanobis distance (Lee et al, 2022). Furthermore, a moving average filter was proposed to consider the last N data points of a time series in a sliding window for support vector regression (SVR) training to remove the USBL outliers (Liu et al, 2019).…”