On the Joint Time Synchronization and Source Localization Using TOA Measurements

Ming, Sun; Yang, Le

doi:10.1155/2013/794805

Cited by 22 publications

(12 citation statements)

References 34 publications

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“…This approach requires the knowledge of signal departure time and thus synchronization between the transmitter and receivers [3]. The unknown time of emission may alternatively be estimated together with the transmitter position [4].…”

Section: Introductionmentioning

confidence: 99%

Iterative ToA-based terminal positioning in emerging cellular systems

Horlin

Eeckhaute

Vorst

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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Abstract-Emerging cellular networks integrate the user terminal geo-localization function besides the communication function. The conventional positioning approach is to estimate the terminal location in two-steps: first the distance to all connected base stations is assessed based on signal time-of-flight measurements, then the location is deduced from the distances by multilateration. The two-step approach incurs a performance degradation because information is lost from the received signal when the multi-lateration is performed. In this paper, we propose to iterate between the two conventional steps to progressively refine the distance estimates based on the knowledge of the position estimate obtained from the previous iterations. The information exchanged between the two-steps not only consists in the mean of the estimates (distance or position) but also of their variance that convey information about the reliability of the estimates. Simulation results show that the achievable performance after a few iterations is close to the performance of the optimal approach that directly estimates the position based on the observation of the received signal.

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Section: Introductionmentioning

confidence: 99%

Iterative ToA-based terminal positioning in emerging cellular systems

Horlin

Eeckhaute

Vorst

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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“…If the time and frequency offsets are small, they can be neglected in source geolocation and this would generally lead to biased source position estimates (see e.g., the analysis in [13]). When they have large absolute values, existing TDOA-FDOA geolocation algorithms such as those developed in [3], [8], [14] generally fail to produce a reasonable solution.…”

Section: Introductionmentioning

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%

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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“…Recently, Zhu and Ding [16] generalized Bancroft's algorithm to the case where more sensors are available, but their technique was shown to be unable to reach the Cramér-Rao lower bound (CRLB) accuracy in general. This motivated the following development of approximately efficient and closed-form methods in [17,18] for the joint synchronization and source localization task. Besides the closed-form solutions, iterative maximum-likelihood (ML) estimators can also be established using e.g., the first-order Taylor series expansion [19], when the knowledge on the probability density function (PDF) of the TOA measurement noises is available.…”

Section: Introductionmentioning

confidence: 99%