TOA-based joint synchronization and source localization with random errors in sensor positions and sensor clock biases

Wang, Ying-Gui; Huang, Jun; Yang, Le; Xue, Yusheng

doi:10.1016/j.adhoc.2014.12.001

Cited by 26 publications

(27 citation statements)

References 29 publications

(68 reference statements)

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“…Similar assumption was also utilized in [17]- [19], [24], [29]. However, as pointed out in the previous subsection, the algorithm implementation uses the estimated source position instead to produce D. The amount of error introduced is dependent on the TDOA and FDOA noise as well as satellite location errors.…”

Section: B Performance Analysismentioning

confidence: 99%

“…T , which is assumed to be zeromean Gaussian distributed with covariance matrix Q β [14], [17]- [19], [24], [29].…”

Section: Problem Formulationmentioning

confidence: 99%

“…In [13], [22]- [25], several techniques were proposed to deal with the problem of node localization in the presence of unknown clock offset in sensor networks. However, they all involved joint time synchronization and node localization based on iterative convex optimization [22] or closed-form methods [13], [23], [24]. Moreover, except for [24], they assumed accurate sensor locations.…”

Section: Introductionmentioning

confidence: 99%

“…However, they all involved joint time synchronization and node localization based on iterative convex optimization [22] or closed-form methods [13], [23], [24]. Moreover, except for [24], they assumed accurate sensor locations.…”

Section: Introductionmentioning

confidence: 99%

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Dual-satellite source geolocation with time and frequency offsets and satellite location errors

Liu

Yang

Mihaylova

2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-This paper considers locating a static source on Earth using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained by a dualsatellite geolocation system. The TDOA and FDOA from the source are subject to unknown time and frequency offsets because the two satellites are imperfectly time-synchronized or frequencylocked. The satellite locations are not known accurately as well. To make the source position identifiable and mitigate the effect of satellite location errors, calibration stations at known positions are used. Achieving the maximum likelihood (ML) geolocation performance usually requires jointly estimating the source position and extra variables (i.e., time and frequency offsets as well as satellite locations), which is computationally intensive. In this paper, a novel closed-form geolocation algorithm is proposed. It first fuses the TDOA and FDOA measurements from the source and calibration stations to produce a single pair of TDOA and FDOA for source geolocation. This measurement fusion step eliminates the time and frequency offsets while taking into account the presence of satellite location errors. The source position is then found via standard TDOA-FDOA geolocation. The developed algorithm has low complexity and performance analysis shows that it attains the Cramér-Rao lower bound (CRLB) under Gaussian noises and mild conditions. Simulations using a challenging scenario with a short-baseline dual-satellite system verify the theoretical developments and demonstrate the good performance of the proposed algorithm.

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Section: B Performance Analysismentioning

confidence: 99%

“…T , which is assumed to be zeromean Gaussian distributed with covariance matrix Q β [14], [17]- [19], [24], [29].…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dual-satellite source geolocation with time and frequency offsets and satellite location errors

Liu

Yang

Mihaylova

2017

2017 20th International Conference on Information Fusion (Fusion)

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“…The posterior probability density function of the source is approximated using Monte Carlo method. A mean square error analysis method [6] is conducted to neglecting sensor position error and clock bias to improve the localization accuracy. A decentralized energy ratios based acoustic source localization method using the incremental gradient algorithm and normalized incremental gradient algorithm is proposed [7].…”

Section: Introductionmentioning

confidence: 99%

Affinity Propagation Algorithm Based Multi-Source Localization Method for Binary Detection

Wang

Cheng

Zhang

2017

IEICE Trans. Inf. & Syst.

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SUMMARYWireless sensor network (WSN) has attracted many researchers to investigate it in recent years. It can be widely used in the areas of surveillances, health care and agriculture. The location information is very important for WSN applications such as geographic routing, data fusion and tracking. So the localization technology is one of the key technologies for WSN. Since the computational complexity of the traditional source localization is high, the localization method can not be used in the sensor node. In this paper, we firstly introduce the Neyman-Pearson criterion based detection model. This model considers the effect of false alarm and missing alarm rate, so it is more realistic than the binary and probability model. An affinity propagation algorithm based localization method is proposed. Simulation results show that the proposed method provides high localization accuracy.

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Joint Position Estimation and Synchronization of Clocks in WSN

Tabassum

Geetha

Biradar

2021

Proceedings of Sixth International Congress on Information and Communication Technology

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TOA-based joint synchronization and source localization with random errors in sensor positions and sensor clock biases

Cited by 26 publications

References 29 publications

Dual-satellite source geolocation with time and frequency offsets and satellite location errors

Dual-satellite source geolocation with time and frequency offsets and satellite location errors

Affinity Propagation Algorithm Based Multi-Source Localization Method for Binary Detection

Joint Position Estimation and Synchronization of Clocks in WSN

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