3D Underwater Sensor Network Localization

Teymorian, A.Y.; Cheng, Wei; Ma, Liran; Cheng, Xiuzhen; Lu, Xicheng; Lu, Zexin

doi:10.1109/tmc.2009.80

Cited by 183 publications

(100 citation statements)

References 30 publications

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“…One example describes the relationship between optimal distance and distance measurement noise, and the relationship between cost function and distance between AUV and USV in different distance measurement noises is analyzed in the other example. From equation (28), the optimal distances corresponding to parameter h are shown in Figure 6. Simulation results show that the extremum values of optimal distance are reached when h = 0:009 m À1 and h = 0:013 m À1 .…”

Section: Influence Of Distance Measurement Noisementioning

confidence: 99%

Optimal distance for moving long baseline positioning system with distance-dependent measurement noise

Chen

Sun

2018

Advances in Mechanical Engineering

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This article investigates the effect of distance-dependent noise on optimal transponder distance in a moving long baseline positioning system. First, according to the positioning model of the moving long baseline positioning system, the relationship between measurement noises and positioning error is established. Second, a cost function is proposed to evaluate the positioning performance. Then, based on the optimal formation, the optimal distance is deduced. Finally, the relationship between the optimal distance and the parameter for the distance-dependent error is proposed. By using this relationship, the effect of distance-dependent measurement noise on optimal distance is analyzed. Simulation examples illustrate the results.

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Section: Influence Of Distance Measurement Noisementioning

confidence: 99%

Optimal distance for moving long baseline positioning system with distance-dependent measurement noise

Chen

Sun

2018

Advances in Mechanical Engineering

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“…A number of real-time and distributed localization techniques have been proposed for underwater networks [4,9,5,6]. Among these DNRL [4] and LSL [6] use multi-lateration while USP [5] applies iterative bilateration. To improve the localization accuracy, DNRL proposes the use of mobile beacons.…”

Section: Related Workmentioning

confidence: 99%

“…Since devices are stationary, the constraints can be considered concurrently to solve for the unknown locations. In principle these algorithms can be extended to mobile underwater networks by localizing time snapshots of the network as suggested in [4][5][6]. However this requires measurements of relative position to be obtained concurrently.…”

Section: Introductionmentioning

confidence: 99%

Real-time collaborative tracking for underwater networked systems

Mirza

Schurgers

Kastner

2012

Proceedings of the Seventh ACM International Conference on Underwater Networks and Systems - WUWNet '12

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a b s t r a c tLocalization 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 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 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.

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“…Analysis on localization schemes focusing on the 3-D space is of particular interests to real applications of WSNs, especially when the difference between 2-D space and 3-D space is significant. For instance, irregularity of the radio transmission has been investigated in 2-D space, while its counterpart in 3-D space remains to be unknown [68,69].…”

Section: -Dimension Localizationmentioning

confidence: 99%

Algorithmic Aspects of Sensor Localization

Das

Wang

Ghosh

et al. 2010

Monographs in Theoretical Computer Science. An EATCS Series

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Identifying locations of nodes in wireless sensor networks (WSNs) is critical to both network operations and most application level tasks. Sensor nodes equipped with geographical positioning system (GPS) devices are aware of their locations at a precision level of few meters. However, installing GPS devices on a large number of sensor nodes is not only expensive but affects the form factor of these nodes. Moreover, GPS-based localization is not applicable in the indoor environments such as buildings. There exists an extensive body of research literature that aims at obtaining absolute locations as well as relative spatial locations of nodes in a WSN without requiring specialized hardware at large scale. The typical approach consists of employing only a limited number of anchor nodes that are aware of their own locations, and then trying to infer locations of non-anchor nodes using graph theoretic, geometric, statistical, optimization and machine learning techniques. Thus, the literature represents a very rich ensemble of algorithmic techniques applicable to low power, highly distributed nodes with resource-optimal computations. In this chapter we take a close look at the algorithmic aspects of various important localization techniques for WSNs.

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3D Underwater Sensor Network Localization

Cited by 183 publications

References 30 publications

Optimal distance for moving long baseline positioning system with distance-dependent measurement noise

Optimal distance for moving long baseline positioning system with distance-dependent measurement noise

Real-time collaborative tracking for underwater networked systems

Algorithmic Aspects of Sensor Localization

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