Accurate Shallow and Deep Water Range Estimation for Underwater Networks

Ahmed, Akram Ben; Younis, Mohamed

doi:10.1109/glocom.2014.7417786

Cited by 2 publications

(4 citation statements)

References 18 publications

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“…Then, the relationship between d and z can be expressed as d = kz, where k is the introduced scale factor. Subsequently, by replacing all anchor-AUV horizontal distances and angles in (21) with k and z, the tr(CRLB) can be rearranged as a function in k for a given z, as shown in (26) at the bottom of this page. In this way, getting the anchor-AUV range is equivalent to finding k, which can be obtained by solving the following optimization problem Algorithm 1 Gradient descent algorithm.…”

Section: Theorem 3 One Of Solutions That Meets C1-c9 Is Given Bymentioning

confidence: 99%

“…By above transformation, the originally five optimization variables in (21), i.e., α i , θ i , ϕ i , and d i , have been converted into a single k in (27). We adopt a gradient descent method to find the promising k under the USC geometry, the details of which are summarized in Algorithm 1.…”

Section: : Initializationmentioning

confidence: 99%

“…From Problem (21), to achieve possibly high estimation accuracy, the first and foremost requirement is that the anchor-AUV geometry should meet the constraints C1-C9 shown in ( 14), (18), and (21), where C1-C9 are a set of multivariable nonlinear equations. Although it is rather complex to mathematically quantify the complete solutions for C1-C9, we interestingly find that an easy-to-implement anchor-AUV geometrical shape, as a special solution for them, can be obtained by exploiting a semi-analytical method.…”

Section: Anchor Deployment Solutionsmentioning

confidence: 99%

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Underwater Anchor-AUV Localization Geometries With an Isogradient Sound Speed Profile: A CRLB-Based Optimality Analysis

Zhang

et al. 2018

IEEE Trans. Wireless Commun.

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Existing works have explored the anchor deployment for autonomous underwater vehicles (AUVs) localization under the assumption that the sound propagates straightly underwater at a constant speed. Considering that the underwater acoustic waves propagate along bent curves at varying speeds in practice, it becomes much more challenging to determine a proper anchor deployment configuration. In this paper, taking the practical variability of underwater sound speed into account, we investigate the anchor-AUV geometry problem in a 3-D timeof-flight (ToF) based underwater scenario from the perspective of localization accuracy. To address this problem, we first rigorously derive the Jacobian matrix of measurement errors to quantify the Cramer-Rao lower bound (CRLB) with a widely-adopted isogradient sound speed profile (SSP). We then formulate an optimization problem that minimizes the trace of the CRLB subject to the angle and range constraints to figure out the anchor-AUV geometry, which is multivariate and nonlinear and thus generally hard to handle. For mathematical tractability, by adopting tools from the estimation theory, we interestingly find that this problem can be equivalently transformed into a more explicit univariate optimization problem. By this, we obtain an easy-to-implement anchor-AUV geometry that yields satisfactory localization performance, referred to as the uniform sea-surface circumference (USC) deployment. Extensive simulation results validate our theoretical analysis and show that our proposed USC scheme outperforms both the cube and the random deployment schemes in terms of localization accuracy under the same parameter settings.Index Terms-Autonomous underwater vehicle (AUV); localization; Cramer-Rao lower bound; anchor deployment.

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Section: Theorem 3 One Of Solutions That Meets C1-c9 Is Given Bymentioning

confidence: 99%

Section: : Initializationmentioning

confidence: 99%

Section: Anchor Deployment Solutionsmentioning

confidence: 99%

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Underwater Anchor-AUV Localization Geometries With an Isogradient Sound Speed Profile: A CRLB-Based Optimality Analysis

Zhang

et al. 2018

IEEE Trans. Wireless Commun.

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“…In this model, Doppler Effect is modeled as variation in the wave number of the field function. Parabolic expansion based modeling techniques are available in literatures [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Doppler Effects in Underwater Acoustic Channels using Parabolic Expansion Modeling

Ranjani¹,

Sadashivappa²

2019

ijacsa

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Underwater communication systems play an important role in understanding various phenomena that take place within our vast oceans. They can be used as an integral tool in countless applications ranging from environmental monitoring to gathering of oceanographic data, marine archaeology, and search and rescue missions. Acoustic Communication is the viable solution for communication in highly attenuating underwater environment. However, these systems pose a number of challenges for reliable data transmission. Nonnegligible Doppler Effect emerges as a major factor. In order to support reliable high data rate communication, understanding the channel behavior is required. As sea trials are expensive, simulators are required to study the channel behavior. Modeling this channel involves solution to wave equations and validation with experimental data for that portion of the sea. Parabolic expansion model is a wave theory based acoustic channel model. This model applies Pade coefficients and Fourier coefficients as expansion functions to solve the wave equations. This work attempts to characterize the impact of Doppler Effect in the underwater acoustic channel using parabolic expansion models.

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Accurate Shallow and Deep Water Range Estimation for Underwater Networks

Cited by 2 publications

References 18 publications

Underwater Anchor-AUV Localization Geometries With an Isogradient Sound Speed Profile: A CRLB-Based Optimality Analysis

Underwater Anchor-AUV Localization Geometries With an Isogradient Sound Speed Profile: A CRLB-Based Optimality Analysis

Analysis of Doppler Effects in Underwater Acoustic Channels using Parabolic Expansion Modeling

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