Set-membership localization and tracking of autonomous underwater vehicles

Algorithms are presented for localization and tracking of Autonomous Underwater Vehicles (AUVs) from acoustic time-of-flight measurements received by a field of surface floating buoys. The algorithms assume that measurements and AUV dynamics uncertainties are unknown but bounded, with known bounds, and produce as output the set in a 3-D space of admissible AUV positions. The algorithms are taylored for a shallow water situation, and account for realistic variations of the sound speed profile in sea water. The algorithms have been validated by simulations in which uncertainty models have been obtained from field data at sea. Localization performance of the algorithm are shown comparable with those previously reported in the literature by other approaches who assume knowledge of the statistics of measurement uncertainties. Tracking performance is shown worse than that of Extended Kalman Filter (EKF) when uncertainties are unbiased. In presence of non-zero mean disturbances (as currents, tides, etc.), the set-membership tracking algorithm outperforms EKF.