Real-time collaborative tracking for underwater networked systems

Localization is a crucial requirement for mobile underwater systems. Real-time 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 collaborative tracking techniques. More specifically the Maximum Likelihood (ML) position estimates of a mobile collective can be computed from measurements of relative positions and motion, albeit centrally and non-real time. While for a number of underwater applications non-real-time position estimates may suffice, in this paper we focus on the design of a collaborative tracking solution that operates in real-time, yet is scalable and energy-efficient. Using the ML solution as a baseline, we identify key factors that fundamentally limit the performance of real-time (centralized and distributed) solutions, quantifying their effects via simulat...

• Type: Journal
• Year: 2015
• Where: ADHOC
• Authors: Diba Mirza, P. Naughton, Curt Schurgers, Ryan Kastner
• Keywords: ADHOC 2015, Applied Computing