Cooperative localization of an acoustic source using towed hydrophone arrays

Abstract

We describe field experiments in which a team of autonomous underwater vehicles cooperatively localize an acoustic source. The team implements a data fusion algorithm to enhance the localization performance of each individual vehicle and implements a decentralized motion control algorithm so that each vehicle maneuvers to minimize the joint localization error of the acoustic source. Each autonomous underwater vehicle is equipped with a custom-designed towed hydrophone array that measures the bearing angle between the array and the acoustic source. The noise statistics of the hydrophone arrays are state-dependent, and a generalized Kalman filter that accounts for the state-dependant measurement noise is utilized for localization.