This paper addresses localization of autonomous\udunderwater vehicles (AUVs) from acoustic time-of-flight measurements received by a field of surface floating buoys. It is assumed\udthat measurements are corrupted by unknown-but-bounded\uderrors, with known bounds. The localization problem is tackled\udin a set-membership framework and an algorithm is presented,\udwhich produces as output the set of admissible AUV positions\udin a three-dimensional (3-D) space. The algorithm is tailored for\uda shallow water situation (water depth less than 500 m), and\udaccounts for realistic variations of the sound speed profile in\udsea water. The approach is validated by simulations in which\uduncertainty models have been obtained from field data at sea.\udLocalization performance of the algorithm are shown comparable\udwith those previously reported in the literature by other approaches who assume knowledge of the statistics of measurement\uduncertainties. Moreover, guaranteed uncertainty regions associated to nominal position estimates are provided. The proposed\udalgorithms can be used as a viable alternative to more traditional\udapproaches in realistic at-sea conditions.
展开▼
