A Review of Underwater Localization Techniques, Algorithms, and Challenges Recently, there has been increasing interest in the field of underwater wireless sensor networks (UWSNs), which is a basic source for the exploration of the ocean environment. A range of military and civilian applications is anticipated to assist UWSN. The UWSN is being developed by the extensive wireless sensor network (WSN) applications and wireless technologies. Therefore, in this paper, a review has been presented which unveils the existing challenges in the underwater environment. In this review, firstly, an introduction to UWSN is presented. After that, underwater localizations and the basics are presented. Secondly, the paper focuses on the architecture of UWSN and technologies used for underwater acoustic sensor network (UASN) localization. Various localization techniques are discussed in the paper classified by centralized and distributed localizations. They are further classified into estimated and predictionbased localizations. Also, various underwater localization algorithms are discussed, which are grouped by the algorithms based on range and rangefree schemes. Finally, the paper focuses on the challenges existing in underwater localizations, underwater acoustic communications with conclusions.

Realtime collaborative tracking for underwater networked systems Localization is a crucial requirement for mobile underwater systems. Realtime position information is needed for control and navigation of underwater vehicles, in early warning systems and for certain routing protocols. Past research has shown that the localization accuracy of networked underwater systems can be significantly improved using intervehicle collaboration. More specifically the Maximum Likelihood (ML) position estimates of a mobile collective can be computed from measurements of relative positions and motion, albeit in a nonrealtime fashion. In this work we extend this solution to compute the position estimates of a network in realtime and in a distributed fashion. We first describe a centralized approach to identify key factors that fundamentally limit the performance of a realtime solution. Using the centralized approach as a benchmark, we arrive at a realtime distributed solution that additionally computes the location of vehicles using information obtained locally by them. We address practical considerations in the implementation of our algorithm and propose solutions to mitigate computational errors. With this proposed implementation, we provide insight on how to appropriately plan a deployment of nodes when collaborative tracking is to be utilized. Lastly, we shed light on situations where implementing collaborative tracking can hinder the localization performance of the network so that these can be avoided.