Self-localization of mobile underwater vector sensor platforms using a source of opportunity

Abstract

Using a network of a few compact mobile underwater platforms, each equipped with a single acoustic sensor, as a distributed sensing array is attractive but requires precise positioning of each mobile sensor. However, traditional accurate underwater positioning tools rely on active acoustic sources (e.g., acoustic pingers), which implies additional hardware and operational complexity. Hence, self-localization (i.e., totally passive) methods using only acoustic sources of opportunity (such as surface vessels) for locating the mobile sensors of a distributed array appear as a simpler alternative. Existing underwater self-localization methods have mainly been developed for mobile platforms equipped with time-synchronized hydrophones and rely only on the time-differences of arrival between multiple pairwise combinations of the mobile hydrophones as inputs for a complex non-linear inversion procedure. Instead, this article introduces a self-localization method, which uses a linear least-square formulation, for two mobile time-synchronized vector sensor platforms based on their acoustic recordings of a distant surface vessel and their inertial navigation system (INS) measurements. This method can be generalized to multiple vector sensor pairs to provide additional robustness toward input parameter errors (e.g., due to a faulty INS) as demonstrated experimentally using drifting buoys with inertial vector sensors deployed ∼100 m apart in shallow water.