Optimal sensor configuration for two dimensional source localization based on TDOA/FDOA measurements

Hamdollahzadeh, Mohammad; Adelipour, Saeed; Behnia, Fereidoon

doi:10.1109/irs.2016.7497276

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…The uncertainty in the position is characterized by the inverse of the CRLB, i.e., the Fisher information matrix (FIM). Therefore, these techniques tries to maximize the FIM or minimize the CRLB by changing the positions of the anchors along the network [12], [29]- [34]. For instance, in [12], the authors use time difference of arrival and frequency difference of arrival ranging methods to optimize the anchor placement for a single target.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, these techniques tries to maximize the FIM or minimize the CRLB by changing the positions of the anchors along the network [12], [29]- [34]. For instance, in [12], the authors use time difference of arrival and frequency difference of arrival ranging methods to optimize the anchor placement for a single target. It was shown in [12] that combination of uniform angular arrays could be used for optimal placement of anchors.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, in [12], the authors use time difference of arrival and frequency difference of arrival ranging methods to optimize the anchor placement for a single target. It was shown in [12] that combination of uniform angular arrays could be used for optimal placement of anchors. A similar analysis with the angle of arrival, time of arrival and RSS-based ranging were presented in [30], [31], and [32], respectively, for a single target where it was shown that there is no unique optimal anchors geometry.…”

Section: Related Workmentioning

confidence: 99%

“…Nonetheless, localization of the underwater smart objects is greatly influenced by the location of anchors. Therefore, the research community on localization came up with different techniques to improve the localization accuracy of a single smart object by optimizing the location of anchors [12], [13]. However, a few works exist on optimizing the location of anchors to reduce the localization error of all smart objects in the network [14].…”

Section: Introductionmentioning

confidence: 99%

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Accurate 3-D Localization of Selected Smart Objects in Optical Internet of Underwater Things

Saeed

Alouini

Al-Naffouri

2020

IEEE Internet Things J.

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Localization is a fundamental task for optical internet of underwater things (O-IoUT) to enable various applications such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for O-IoUT greatly relies on the location of the anchors. Therefore, recently localization techniques for O-IoUT which optimize the anchor's location are proposed. However, optimization of anchors location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this paper, we propose a three-dimensional accurate localization technique by optimizing the anchor's location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable sensors. Numerical results show that the proposed technique of optimizing anchor's location for a set of selected sensors provides a better location accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accurate 3-D Localization of Selected Smart Objects in Optical Internet of Underwater Things

Saeed

Alouini

Al-Naffouri

2020

IEEE Internet Things J.

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“…Therefore, targets are operating underwater and are to be localized in 3D while sensors can only be placed on water surface in 2D as shown in Figure 6. In most of the previous research, sensors as well as targets were considered to be placed in 2D [51][52][53][54]. In the cases where 3D localization of the targets was considered, it was assumed that both sensors and targets can be located anywhere in 3D [27,55].…”

Section: Constraints Of Sensor Placementmentioning

confidence: 99%

A Review on Optimal Placement of Sensors for Cooperative Localization of AUVs

Razzaqi

Farid

2019

Journal of Sensors

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Self-positioning of submerged Autonomous Underwater Vehicles (AUVs) is a challenging task due to nonavailability of GPS signals. One of the most recent solutions for this is the use of surface vehicles (sensors) for cooperative localization of the underwater vehicles (targets) by measuring their relative positions. However, correct placement of the surface sensors is very critical as their geometric configuration affects their observability and hence availability of their relative positions information to the targets. In this paper, a comparative survey of sensors’ optimal formation techniques for cooperative localization of AUVs has been presented. Introduction to the basic cooperative localization techniques and background theory of optimal sensor placements have been provided. This paper can also serve as a fundamental reading material for students and researchers pursuing research on optimal sensor placement for cooperative localization.

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Optimal Sensor Deployment and Velocity Configuration With Hybrid TDOA and FDOA Measurements

et al. 2019

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Source localization based on the hybrid time-difference-of-arrival (TDOA) and frequencydifference-of-arrival (FDOA) measurements from distributed sensors is an essential problem in wireless sensor networks (WSNs). In this paper, we mainly study the optimal sensor deployment and velocity configuration of UAV swarms mounted with TDOA and FDOA based sensors. Explicit solutions of optimal sensor deployment and velocity configuration are acquired in both static and movable source scenarios based on the Fisher information matrix (FIM). Both centralized and decentralized localization are explored to meet different types of localization methods. Path planning problem of UAV swarms in TDOA/FDOA localization is also studied with constraints. Simulations verify its efficiency with path planning in TDOA and FDOA localization. INDEX TERMS Time-difference-of-arrival (TDOA), frequency-difference-of-arrival (FDOA), sensor deployment, fisher information matrix, path planning.

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Optimal sensor configuration for two dimensional source localization based on TDOA/FDOA measurements

Cited by 9 publications

References 17 publications

Accurate 3-D Localization of Selected Smart Objects in Optical Internet of Underwater Things

Accurate 3-D Localization of Selected Smart Objects in Optical Internet of Underwater Things

A Review on Optimal Placement of Sensors for Cooperative Localization of AUVs

Optimal Sensor Deployment and Velocity Configuration With Hybrid TDOA and FDOA Measurements

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