Using a source of opportunity for self-localization of mobile underwater vector sensor platforms

Abstract

Transforming a network of few compact mobile underwater platforms, each equipped with a single acoustic sensor, into a distributed sensing array requires precise positioning of each mobile sensor. But conventional accurate underwater positioning tools rely on active acoustic sources (e.g., acoustic pingers), which imposes 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 an attractive alternative. Existing underwater self-localization methods have mainly been developed for mobile platform equipped with time-synchronized hydrophones and rely only on the time-difference of arrivals between multiple pairwise combinations of the mobile hydrophones as inputs for a complex non-linear inversion procedure. Instead, we introduce 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 systems (INS) measurements. This method can be generalized to multiple vector sensor pairs to provide additional robustness towards 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.