UC San Diego

Knowing the transponder location for Autonomous Underwater Vehicle (AUV) navigationis imperative to predicting the location of the AUV during mission navigation. Accurate underwater navigation requires external transponder beacons or navigation aids with known locations. Traditionally, the localization method for these transponder beacons is normally done with ship-based surveys that take time and personnel. This thesis proposes that using autonomous vehicles, specifically surface vehicles, to perform the transponder survey will free up personnel, save time, and yield accurate and precise estimates of the transponder location. This thesis looks to automate the process of transponder beacon navigation. xi Two approaches are applied and developed to perform localization: a least squares method and an extended Kalman Filter. These approaches are tested on field data collected by a Boeing Liquid Robotics Wave Glider equipped with a WHOI micromodem for acoustic communications and a Global Positioning System (GPS) receiver. Two field experiments were conducted in La Jolla, California in shallow water, and included varying geometries of the survey path used for localization. The Kalman Filter outperforms the least squares method for precision, and it identifies the error bounds of the estimate. It includes a motion model specific to the physical movement of the Wave Glider’s sub and float components. Finally, the Kalman filter provides an on-board algorithm that can be run in real-time without excessive usage of data storage, which the least squares method would require. Both theoretical and data analysis conclude that traveling a 150-degree arc around the drop location will allow the area of uncertainty and transponder position covariance error ellipse to converge to a steady state value. Traveling straight line transects will also yield precise survey results and may diminish the total time it takes to perform the localization survey.