Joint Time Synchronization and Localization of an Unknown Node in Wireless Sensor Networks

Zheng, Jun; Wu, Yik-Chung

doi:10.1109/tsp.2009.2032990

Cited by 167 publications

(153 citation statements)

References 25 publications

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“…However, the proposed least squares (LS) estimator in [22] cannot reach the CRLB accuracy. As a future topic, we would like to derive an efficient closed-form estimator for joint synchronization and source localization in the presence of time skew.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, synchronization is usually achieved first via applying one of the available protocoldomain techniques, such as [15][16][17], and the localization task can then be accomplished by executing various algorithms, such as those developed in [7][8][9][12][13][14]. A more recent trend is to perform joint synchronization and source localization owing to their close relationship [18][19][20][21][22]. For this problem, given the statistical model of the TOA measurement noise, the maximum likelihood estimator (MLE) can be developed.…”

Section: Introductionmentioning

confidence: 99%

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On the Joint Time Synchronization and Source Localization Using TOA Measurements

Ming

Yang

2013

International Journal of Distributed Sensor Networks

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This paper considers the problem of estimating the clock bias and the position of an unknown source using time of arrival (TOA) measurements obtained at a sensor array to achieve time synchronization and source localization. The study starts with deriving the localization mean square error (MSE) for the case where we pretend that the source clock bias is absent and apply TOA positioning to find the source position. An upper bound on the clock bias, over which we shall obtain a higher localization MSE than that from jointly identifying the clock bias with the source position, is established. Motivated by the MSE analysis, this paper proceeds to develop a new efficient solution for joint synchronization and source localization. The new method is in closedform, computationally attractive, and more importantly; it is shown analytically to attain the CRLB accuracy under small Gaussian TOA measurement noise. Computer simulations are conducted to corroborate the theoretical development and illustrate the good performance of the proposed algorithm.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Joint Time Synchronization and Source Localization Using TOA Measurements

Ming

Yang

2013

International Journal of Distributed Sensor Networks

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“…Prior knowledge about the node positions in the network is modeled as x i ∼ N (μ i , C i ), where the nominal position μ i and error covariance matrix C i is set for all i [5][6][7]. With C −1 i = 0 we can also model complete ignorance of a node position.…”

Section: Figurementioning

confidence: 99%

“…The common challenges in location estimation are measurement noise, availability of accurate timing models and anchor uncertainty. Authors in [5][6][7][8] have proposed estimation methods and algorithms which are robust to anchor and timing uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Schedule-based sequential localization in asynchronous wireless networks

Zachariah

Angelis

Dwivedi

et al. 2014

EURASIP J. Adv. Signal Process.

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In this paper, we consider the schedule-based network localization concept, which does not require synchronization among nodes and does not involve communication overhead. The concept makes use of a common transmission sequence, which enables each node to perform self-localization and to localize the entire network, based on noisy propagation-time measurements. We formulate the schedule-based localization problem as an estimation problem in a Bayesian framework. This provides robustness with respect to uncertainty in such system parameters as anchor locations and timing devices. Moreover, we derive a sequential approximate maximum a posteriori (AMAP) estimator. The estimator is fully decentralized and copes with varying noise levels. By studying the fundamental constraints given by the considered measurement model, we provide a system design methodology which enables a scalable solution. Finally, we evaluate the performance of the proposed AMAP estimator by numerical simulations emulating an impulse-radio ultra-wideband (IR-UWB) wireless network.

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“…Practical implementations and the combination of recently proposed methods from both fields are analyzed. In Chapter 6, an overview of simultaneous approaches is presented, with a distinction in centralized and distributed computation [1,19,26,35,36,37,75,89,124,129,137,138,139,141,143,144]. In this review we provide a comprehensive orientation in this novel topic.…”

Section: Introductionmentioning

confidence: 99%