Distributed Gauss-Newton method for localization in Ad-hoc networks

Béjar, Benjamín; Belanović, Pavle; Zazo, Santiago

doi:10.1109/acssc.2010.5757776

Cited by 19 publications

(25 citation statements)

References 5 publications

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“…This problem has been studied in [17,18] where a distributed version of the Gauss-Newton method can be used for its solution. In this study, we present a more flexible approach that uses concepts from convex optimization theory.…”

Section: Practical Approachmentioning

confidence: 99%

A practical approach for outdoors distributed target localization in wireless sensor networks

Béjar

Zazo

2012

EURASIP J. Adv. Signal Process.

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Wireless sensor networks are posed as the new communication paradigm where the use of small, low-complexity, and low-power devices is preferred over costly centralized systems. The spectra of potential applications of sensor networks is very wide, ranging from monitoring, surveillance, and localization, among others. Localization is a key application in sensor networks and the use of simple, efficient, and distributed algorithms is of paramount practical importance. Combining convex optimization tools with consensus algorithms we propose a distributed localization algorithm for scenarios where received signal strength indicator readings are used. We approach the localization problem by formulating an alternative problem that uses distance estimates locally computed at each node. The formulated problem is solved by a relaxed version using semidefinite relaxation technique. Conditions under which the relaxed problem yields to the same solution as the original problem are given and a distributed consensusbased implementation of the algorithm is proposed based on an augmented Lagrangian approach and primaldual decomposition methods. Although suboptimal, the proposed approach is very suitable for its implementation in real sensor networks, i.e., it is scalable, robust against node failures and requires only local communication among neighboring nodes. Simulation results show that running an additional local search around the found solution can yield performance close to the maximum likelihood estimate.

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Section: Practical Approachmentioning

confidence: 99%

A practical approach for outdoors distributed target localization in wireless sensor networks

Béjar

Zazo

2012

EURASIP J. Adv. Signal Process.

Self Cite

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show abstract

“…However, due to the noisy measurement and estimation errors, we need to minimize the cost function. The estimation of target position can be rewritten as an optimization problem (5) and centralized optimization methods such as [18] or distributed algorithms such as Gauss Newton [16] may be used to solve the problem (5).…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…Distributed Gauss-Newton localization is robust against shadowing effect, uncertainty in position of sensor nodes and network topology changes for target localization [16].…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…The proposed algorithm in [15] considers a fully decentralized sensor network to estimate the target location using RSS, however, many 513 practical limitations such as shadowing effect is not considered. In [16] authors introduce a consensus based decentralized Gauss Newton localization algorithm. In [16] all nodes cooperate and share their information to finally make decision on the location of target node.…”

Section: Introductionmentioning

confidence: 99%

“…In [16] authors introduce a consensus based decentralized Gauss Newton localization algorithm. In [16] all nodes cooperate and share their information to finally make decision on the location of target node. Diffusion based strategy is recently proposed which is a fully distributed decision making process and outperforms the other distributed methods including the consensus [17].…”

Section: Introductionmentioning

confidence: 99%

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