A TDOA underwater localization approach for shallow water environment

Kouzoundjian, Baptiste; Beaubois, Florian; Reboul, Serge; Choquel, Jean-Bernard; Noyer, Jean-Charles

doi:10.1109/oceanse.2017.8085030

Cited by 18 publications

(16 citation statements)

References 5 publications

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“…Localization of underwater acoustic networks has been investigated in [352] by using Hausdorff distances for TDoA ranging. Localization in shallow water using TDoA measurements was achieved in [353] by considering two hydrophones mounted under the boat. In [354], the authors have implemented an accurate and precise underwater acoustic localization system by using TDoA measurements with the help of a microphone and a speaker.…”

Section: A Basics Of Underwater Localizationmentioning

confidence: 99%

Underwater optical wireless communications, networking, and localization: A survey

et al. 2019

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Underwater wireless communications can be carried out through acoustic, radio frequency (RF), and optical waves. Compared to its bandwidth limited acoustic and RF counterparts, underwater optical wireless communications (UOWCs) can support higher data rates at low latency levels. However, severe aquatic channel conditions (e.g., absorption, scattering, turbulence, etc.) pose great challenges for UOWCs and significantly reduce the attainable communication ranges, which necessitates efficient networking and localization solutions. Therefore, we provide a comprehensive survey on the challenges, advances, and prospects of underwater optical wireless networks (UOWNs) from a layer by layer perspective which includes: 1) Potential network architectures; 2) Physical layer issues including propagation characteristics, channel modeling, and modulation techniques 3) Data link layer problems covering link configurations, link budgets, performance metrics, and multiple access schemes; 4) Network layer topics containing relaying techniques and potential routing algorithms; 5) Transport layer subjects such as connectivity, reliability, flow and congestion control; 6) Application layer goals and state-of-the-art UOWN applications, and 7) Localization and its impacts on UOWN layers. Finally, we outline the open research challenges and point out the future directions for underwater optical wireless communications, networking, and localization research.

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Section: A Basics Of Underwater Localizationmentioning

confidence: 99%

Underwater optical wireless communications, networking, and localization: A survey

et al. 2019

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“…In such environments, the speed of sound is (at least approximately) constant 1 and known in the relevant volume of interest. Therefore, propagation delay, namely time of arrival (TOA) or time-difference of arrival (TDOA) (e.g., [10], [11]), is usually employed as a basis for different localization methods [12]. However, since the underwater acoustic environment typically induces a rich multipath channel [13], the measured signals contain both LOS and NLOS components.…”

Section: A Related Work: Underwater Acoustic Localizationmentioning

confidence: 99%

“…. , b L means that any relevant physical parameter, such as the ocean bottom sediment coefficient κ b in (11), is assumed to be perfectly known as well. We relax this (somewhat unrealistic) assumption in our semi-blind localization approach described next.…”

Section: The Matched Field Processing Solutionmentioning

confidence: 99%

A Semi-Blind Method for Localization of Underwater Acoustic Sources

Weiss,

Arikan,

Vishnu

et al. 2021

Preprint

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Underwater acoustic localization has traditionally been challenging due to the presence of unknown environmental structure and dynamic conditions. The problem is richer still when such structure includes occlusion, which causes the loss of line-of-sight (LOS) between the acoustic source and the receivers, on which many of the existing localization algorithms rely. We develop a semi-blind passive localization method capable of accurately estimating the source's position even in the possible absence of LOS between the source and all receivers. Based on typically-available prior knowledge of the water surface and bottom, we derive a closed-form expression for the optimal estimator under a multi-ray propagation model, which is suitable for shallow-water environments and high-frequency signals. By exploiting a computationally efficient form of this estimator, our methodology makes comparatively high-resolution localization feasible. We also derive the Cramér-Rao bound for this model, which can be used to guide the placement of collections of receivers so as to optimize localization accuracy. The method improves a balance of accuracy and robustness to environmental model mismatch, relative to existing localization methods that are useful in similar settings. The method is validated with simulations and water tank experiments.

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“…In [15] authors have been proposed an acoustic-based localization method for positioning algorithm in the shallow water environment. In this literature, the authors propose a closed-loop tracking algorithm based on Kalman filtering to track the peak of the time correlation of the received signals using two hydrophones.…”

Section: Related Workmentioning

confidence: 99%

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Sun

Esmaiel

2020

Sensors

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Underwater acoustic localization is a useful technique applied to any military and civilian applications. Among the range-based underwater acoustic localization methods, the time difference of arrival (TDOA) has received much attention because it is easy to implement and relatively less affected by the underwater environment. This paper proposes a TDOA-based localization algorithm for an underwater acoustic sensor network using the maximum-likelihood (ML) ratio criterion. To relax the complexity of the proposed localization complexity, we construct an auxiliary function, and use the majorization-minimization (MM) algorithm to solve it. The proposed localization algorithm proposed in this paper is called a T-MM algorithm. T-MM is applying the MM algorithm to the TDOA acoustic-localization technique. As the MM algorithm iterations are sensitive to the initial points, a gradient-based initial point algorithm is used to set the initial points of the T-MM scheme. The proposed T-MM localization scheme is evaluated based on squared position error bound (SPEB), and through calculation, we get the SPEB expression by the equivalent Fisher information matrix (EFIM). The simulation results show how the proposed T-MM algorithm has better performance and outperforms the state-of-the-art localization algorithms in terms of accuracy and computation complexity even under a high presence of underwater noise.

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A TDOA underwater localization approach for shallow water environment

Cited by 18 publications

References 5 publications

Underwater optical wireless communications, networking, and localization: A survey

Underwater optical wireless communications, networking, and localization: A survey

A Semi-Blind Method for Localization of Underwater Acoustic Sources

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

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