A Semi-Blind Method for Localization of Underwater Acoustic Sources

Weiss, Amir; Arikan, Toros; Vishnu, Hari; Deane, Grant B.; Singer, Andrew C.; Wornell, Gregory W.

doi:10.1109/tsp.2022.3173731

Cited by 16 publications

(9 citation statements)

References 48 publications

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“…In the second phase, these three models are fused into a "global model", that provides an (enhanced) estimate of the source position. The successful operation of this solution has been demonstrated for a 3-ray isovelocity propagation model, which theoretically allows for accurate DLOC even in the absence of LOS (see [22] for simulation and experimental results). While this is already a powerful capability, in this work we demonstrate that this solution generalizes to even richer environments, which are "closer"-in some well-defined sense-to the real physical medium in which UWA localization systems operate.…”

Section: A Data-driven Direct Localization Methodsmentioning

confidence: 98%

“…To go beyond the 3-ray model [22], we generate the CIRs using the Bellhop simulator [21]. This way, the propagation model can be made exceptionally rich, taking into account nontrivial affects such as "bending" rays due to depth-varying speed of sound, different bathymetry and surface geometries, etc.…”

Section: A Data-driven Direct Localization Methodsmentioning

confidence: 99%

“…Given a source-receiver pair, the relevant rays propagating from the source to the receiver can be described in a plane perpendicular to an ideal flat bottom, in which the source and the receiver lie. As an example, the complexity of the method [22] in its extended form [19] is Ω(N L 2 R 2 ) even before considering that all the LR time-delays of all the rays to all the receivers must be computed as well. 4 It is therefore natural to adopt a learning-based solution approach, that will provide a method to localize the source while: (i) judiciously using those environmental characteristics that can be learned during the training phase; (ii) being robust to reasonably moderate deviations from the learned model; and (iii) efficiently approximating a reliable estimator p = f (x1, .…”

Section: Problem Formulationmentioning

confidence: 99%

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Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch

Weiss

Singer

Wornell

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Key challenges in developing underwater acoustic localization methods are related to the combined effects of high reverberation in intricate environments. To address such challenges, recent studies have shown that with a properly designed architecture, neural networks can lead to unprecedented localization capabilities and enhanced accuracy. However, the robustness of such methods to environmental mismatch is typically hard to characterize, and is usually assessed only empirically. In this work, we consider the recently proposed data-driven method [19] based on a deep convolutional neural network, and demonstrate that it can learn to localize in complex and mismatched environments. To explain this robustness, we provide an upper bound on the localization mean squared error (MSE) in the "true" environment, in terms of the MSE in a "presumed" environment and an additional penalty term related to the environmental discrepancy. Our theoretical results are corroborated via simulation results in a rich, highly reverberant, and mismatch channel.

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Section: A Data-driven Direct Localization Methodsmentioning

confidence: 98%

Section: A Data-driven Direct Localization Methodsmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

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Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch

Weiss

Singer

Wornell

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…A semi-blind localization method estimating the position of a source in the absence of line of sight between the receivers and source. The closed-form method is used for high-frequency signals and shallow water environments, 18 deep reinforcement learning method is the solution for hide the private information, reduce non-convex issues in an inhomogeneous environment. 19 Crammer Rao lower bound (CRLB) traces the optimal deployment of anchor nodes in underwater, 20 minimizing the localization noise at higher noise conditions (21).…”

Section: Related Workmentioning

confidence: 99%

Vortex‐induced vibration effects in underwater acoustic wireless sensor networks

Reddy,

Arya

2023

Trans Emerging Tel Tech

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A randomly fluctuated vortex‐induced vibration (VIV) affects the localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) to cause an offset time and time synchronization error which disturbs the localization of the non‐cooperative moving target. Definition 1, Theorem 1 has been introduced in the present work to describe the genesis and consequences of the VIV effect in UAWSN. An elliptic localization introduces the direct and indirect path measurements of the moving target. The standard Crammer Rao lower bound parameter quantifies the VIV effect. The compensation of vortex‐induced vibrations (CVV) algorithm is proposed to quantify the degradation of localization performance and to estimate the position of the moving target. The theoretical measurements of position error, velocity error with respect to the variation of noise, number of receiver sensors, and probability of line of sight errors are compared to the standard methods. The proposed CVV algorithm improves the localization performance by 32.24% and reduces 34.36% of position errors, and 37.46% of velocity errors up to 600 m. It can be useful for the sub‐aquatic internet of underwater things.

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“…Additionally, this paper builds on large-scale field estimation of discretized PDE systems [23], [24], and previous work on source identifiability and estimation in such systems [25], [26]. Further related work focused on USL for shallow-water environments and highfrequency signals using a multi-ray propagation model [27], decentralized detection in underwater sensor networks [28], decentralized USL via generalized likelihood ratio test [29], a self-supervised learning architecture that exploits joint timefrequency processing for USL [30], and acoustic source localization and tracking using a cluster of mobile agents [31].…”

Section: Introductionmentioning

confidence: 99%

Underwater Source Localization via Spectral Element Acoustic Field Estimation

Manduzio,

Forti,

Sabatini

et al. 2023

IEEE Sensors J.

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Underwater source localization from a passive array of acoustic sensors is a challenging problem, especially in complex environments characterized by multipath and reverberation effects, irregular seabed geometry, and low signal-to-noise ratio. This paper proposes a recursive Bayesian approach that propagates a spectral-element approximation of the wave equation to model the discretized space-time dynamics of the acoustic field conditioned on the position of the source, and sequentially estimates the field and the position of the radiating source from the acoustic measurements. We pursue a multiple-model approach where each model assumes either the source absence or its presence within a specific spectral element. To handle the high dimension of the large-scale field estimation problem and reduce the computational complexity, the multiple-model filter is implemented by using ensemble Kalman filters. Finally, the effectiveness of the proposed multiple-model spectral-element ensemble Kalman filter is demonstrated through simulation experiments in underwater acoustic environments with regular and irregular seabed geometry and via comparison with the standard matched-field processing method.

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A Semi-Blind Method for Localization of Underwater Acoustic Sources

Cited by 16 publications

References 48 publications

Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch

Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch

Vortex‐induced vibration effects in underwater acoustic wireless sensor networks

Underwater Source Localization via Spectral Element Acoustic Field Estimation

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