Underwater localization using stochastic proximity embedding and multi-dimensional scaling

Abstract

Localization for underwater acoustic sensor networks is an active research topic where a large number of techniques have been proposed recently. This paper addresses one of the open research issues, the impact of underwater sound speed variation on the localization accuracy. In this paper, modified versions of stochastic proximity embedding and multi-dimensional scaling localization algorithms customized for underwater application are proposed. The algorithms are found to provide good performance in underwater scenario as they take into account refractive ray bending of acoustic waves. Detailed study of the algorithm performance has been done and the results are reported. Cramer Rao Lower Bound for the problem is also derived.