Real-time collaborative tracking for underwater networked systems

Abstract

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 inter-vehicle 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 non-real-time fashion. In this work we extend this solution to compute the position estimates of a network in real-time and in a distributed fashion. We first describe a centralized approach to identify key factors that fundamentally limit the performance of a real-time solution. Using the centralized approach as a benchmark, we arrive at a real-time 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.

Joint time synchronization and tracking for mobile underwater systems

“…The sum-product algorithm can operate on this graph and exploit these simple relations to estimate the pdf of individual states in the graph via iterative message passing [6] [7]. We have previously discussed this framework in the context of underwater tracking [8]. However, in our previous work we used this framework to track submersibles from inter-vehicle measurements of distance.…”

“…A number of tracking techniques estimate a vehicle's position from nonconcurrent distance estimates [8] [14]. However, distance estimation requires round trip message exchange or prior time synchronization with the beacons.…”

Ambient noise correlations on a mobile, deformable array

“…Since the AUEs can move in the period that the acoustic signals are being received, we can either assume that they are stationary during the buoy signal acquisition or we can estimate their motion during the signal reception by employing a factor graph framework. 19,20 We have performed both methods for this dataset and they yield similar trajectories. These estimated trajectories are used throughout the analysis given in this paper.…”

A zone adaptive distance and failure analysis approach for reliable target tracking

“…Author improved the node position using genetic and soft computing approach so that the performance of network will be improved. Diba Mirza [11] has defined a collaborative node tracking approach in sensor network. Author improved the node localization in mobile sensor network.…”