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Underwater Acoustic Remote Sensing for Ocean and Lake Monitoring
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Underwater Acoustic Remote Sensing for Ocean and Lake Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 4342

Special Issue Editors

Dr. Andrew R. Barnard

E-Mail Website
Guest Editor
Graduate Program in Acoustics, College of Engineering, The Pennsylvania State University, University Park, PA 16802, USA
Interests: measurements; data acquisition; underwater acoustics, signal processing; structural acoustics; experimental modal analysis; outdoor sound propagation; acoustic arrays and beamforming; nearfield acoustic holography; sound intensity; architectural acoustics; loudspeaker array design; control systems; stadium/crowd noise
Prof. Dr. Guy Meadows

E-Mail Website
Guest Editor
Great Lakes Research Center, Michigan Technological University, Houghton, MI 49931, USA
Interests: marine engineering; underwater acoustic remote sensing; autonomous and semi-autonomous environmental monitoring platforms; nearshore hydrodynamics and prediction

Special Issue Information

Dear Colleagues,

Underwater acoustic remote sensing has been vital to monitoring and exploring our ocean and lake environments for decades. The burgeoning Blue Economy, coupled with advances in sensing technology and data-driven processing methods is accelerating the need and use cases for underwater acoustic remote sensing systems. In addition, new operating environments in the Arctic and Antarctic are opening up due to climate-driven ice loss, which provides new regions for acoustic exploration and monitoring. This Special Issue focuses on state-of-the-art research in underwater acoustic remote sensing techniques including, but not limited to:

  • Multimodal sensing and data fusion
  • Advanced signal processing methods for underwater acoustic remote sensing including AI-inspired machine learning techniques
  • Passive acoustic sensing
  • Active SONAR development and applications
  • Acoustic array development and processing methods for underwater monitoring
  • Marine ecosystem and soundscape monitoring
  • Monitoring of marine life
  • Anthropogenic underwater noise characterization
  • Ocean- and basin-wide acoustic observation systems
  • Oil and gas exploration and sensing using acoustics
  • Remote acoustic sensing on moving platforms such as autonomous surface and subsurface vehicles
  • Arctic and Antarctic underwater acoustic observations
  • Large lake acoustic remote sensing applications
  • Any other related topic

Dr. Andrew R. Barnard
Prof. Dr. Guy Meadows
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

19 pages, 9279 KiB  
Article
A High–Efficiency Side–Scan Sonar Simulator for High–Speed Seabed Mapping
by Xiangjian Meng, Wen Xu, Binjian Shen and Xinxin Guo
Sensors 2023, 23(6), 3083; https://doi.org/10.3390/s23063083 - 13 Mar 2023
Cited by 1 | Viewed by 2442
Abstract
Side scan sonar (SSS) is a multi–purpose ocean sensing technology, but due to the complex engineering and variable underwater environment, its research process often faces many uncertain obstacles. A sonar simulator can provide reasonable research conditions for guiding development and fault diagnosis, by [...] Read more.
Side scan sonar (SSS) is a multi–purpose ocean sensing technology, but due to the complex engineering and variable underwater environment, its research process often faces many uncertain obstacles. A sonar simulator can provide reasonable research conditions for guiding development and fault diagnosis, by simulating the underwater acoustic propagation and sonar principle to restore the actual experimental scenarios. However, the current open–source sonar simulators gradually lag behind mainstream sonar technology; therefore, they cannot be of sufficient assistance, especially due to their low computational efficiency and unsuitable high–speed mapping simulation. This paper presents a sonar simulator based on a two–level network architecture, which has a flexible task scheduling system and extensible data interaction organization. The echo signal fitting algorithm proposes a polyline path model to accurately capture the propagation delay of the backscattered signal under high–speed motion deviation. The large–scale virtual seabed is the operational nemesis of the conventional sonar simulators; therefore, a modeling simplification algorithm based on a new energy function is developed to optimize the simulator efficiency. This paper arranges several seabed models to test the above simulation algorithms, and finally compares the actual experiment results to prove the application value of this sonar simulator. Full article
(This article belongs to the Special Issue Underwater Acoustic Remote Sensing for Ocean and Lake Monitoring)
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21 pages, 5786 KiB  
Article
3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference
by Minseuk Park, Sufyan Ali Memon, Geunhwan Kim and Youngmin Choo
Sensors 2023, 23(5), 2628; https://doi.org/10.3390/s23052628 - 27 Feb 2023
Viewed by 1378
Abstract
The propeller tip vortex cavitation (TVC) localization problem involves the separation of noise sources in proximity. This work describes a sparse localization method for off-grid cavitations to estimates their precise locations while keeping reasonable computational efficiency. It adopts two different grid (pairwise off-grid) [...] Read more.
The propeller tip vortex cavitation (TVC) localization problem involves the separation of noise sources in proximity. This work describes a sparse localization method for off-grid cavitations to estimates their precise locations while keeping reasonable computational efficiency. It adopts two different grid (pairwise off-grid) sets with a moderate grid interval and provides redundant representations for adjacent noise sources. To estimate the position of the off-grid cavitations, a block-sparse Bayesian learning-based method is adopted for the pairwise off-grid scheme (pairwise off-grid BSBL), which iteratively updates the grid points using Bayesian inference. Subsequently, simulation and experimental results demonstrate that the proposed method achieves the separation of adjacent off-grid cavitations with reduced computational cost, while the other scheme suffers from a heavy computational burden; for the separation of adjacent off-grid cavitations, the pairwise off-grid BSBL took significantly less time (29 s) compared with the time taken by the conventional off-grid BSBL (2923 s). Full article
(This article belongs to the Special Issue Underwater Acoustic Remote Sensing for Ocean and Lake Monitoring)
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