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Recent Advances in Underwater Signal Processing II
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Recent Advances in Underwater Signal Processing II

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 2966

Special Issue Editors

Dr. Haixin Sun

E-Mail Website
Guest Editor
Deportment of Information and Communication Engineering, Xiamen University, 422 Siming South Road, Xiamen 361005, China
Interests: digital communications; wireless communications; modern signal processing; underwater acoustic communication
Special Issues, Collections and Topics in MDPI journals
Dr. Xuebo Zhang

E-Mail Website
Guest Editor
College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
Interests: sonar imaging; synthetic aperture sonar; synthetic aperture radar; image resolution; radar imaging; signal reconstruction; signal sampling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue “Recent Advances in Underwater Signal Processing” (https://www.mdpi.com/journal/sensors/special_issues/raudsp_sensors), we are pleased to announce the next in the series, entitled “Recent Advances in Underwater Signal Processing II”.

In total, 71% of the Earth is covered by ocean, which plays an important role in human life (ecological regulation, living resources, mineral resources, etc.). Underwater equipment, including sonar and radar, can help us to better understand the ocean. Using these technologies, topography, underwater communication, target detection, localization, imaging, and ocean monitoring can be easily carried out. Signal processing and electronics techniques have achieved significant progress in recent years. Thanks to these developments, the novel theories, mechanisms, and processing techniques of underwater equipment have also been pushed into a new stage.

This Special Issue aims to highlight recent advancements, developments, and applications in underwater signal processing methodologies, including characterization, simulation, real data processing, as well as applications to underwater engineering. In general, any contributions related to underwater signal processing or ocean signal processing will be considered.

Potential topics include but are not limited to the following:

  • Underwater communication;
  • Underwater network;
  • Underwater detection;
  • Underwater navigation;
  • Underwater noise modeling;
  • Underwater mapping and localization;
  • Underwater vehicle technology;
  • Sonar signal processing;
  • Ocean monitoring;
  • Ocean remote sensing techniques;
  • Marine environment assessment;
  • Air–sea interactions.

Dr. Haixin Sun
Dr. Xuebo Zhang
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.

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Published Papers (2 papers)

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Research

19 pages, 6506 KiB  
Article
An Underwater Image Denoising Method Based on High-Frequency Abrupt Signal Separation and Hybrid Attention Mechanism
by Chunling Huo, Da Zhang and Huanyu Yang
Sensors 2024, 24(14), 4578; https://doi.org/10.3390/s24144578 - 15 Jul 2024
Cited by 1 | Viewed by 899
Abstract
During underwater image processing, image quality is affected by the absorption and scattering of light in water, thus causing problems such as blurring and noise. As a result, poor image quality is unavoidable. To achieve overall satisfying research results, underwater image denoising is [...] Read more.
During underwater image processing, image quality is affected by the absorption and scattering of light in water, thus causing problems such as blurring and noise. As a result, poor image quality is unavoidable. To achieve overall satisfying research results, underwater image denoising is vital. This paper presents an underwater image denoising method, named HHDNet, designed to address noise issues arising from environmental interference and technical limitations during underwater robot photography. The method leverages a dual-branch network architecture to handle both high and low frequencies, incorporating a hybrid attention module specifically designed for the removal of high-frequency abrupt noise in underwater images. Input images are decomposed into high-frequency and low-frequency components using a Gaussian kernel. For the high-frequency part, a Global Context Extractor (GCE) module with a hybrid attention mechanism focuses on removing high-frequency abrupt signals by capturing local details and global dependencies simultaneously. For the low-frequency part, efficient residual convolutional units are used in consideration of less noise information. Experimental results demonstrate that HHDNet effectively achieves underwater image denoising tasks, surpassing other existing methods not only in denoising effectiveness but also in maintaining computational efficiency, and thus HHDNet provides more flexibility in underwater image noise removal. Full article
(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing II)
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18 pages, 9488 KiB  
Article
A High-Resolution Imaging Method for Multiple-Input Multiple-Output Sonar Based on Deterministic Compressed Sensing
by Ning Gao, Feng Xu and Juan Yang
Sensors 2024, 24(4), 1296; https://doi.org/10.3390/s24041296 - 17 Feb 2024
Cited by 1 | Viewed by 1052
Abstract
Differences between conventional sonar and Multiple-Input Multiple-Output (MIMO) sonar systems arise in achieving high angular and range resolution. MIMO sonar uses Matched Filtering (MF) with well-correlated transmitted signals to enhance spatial resolution by obtaining virtual arrays. However, imperfect correlation characteristics yield high sidelobe [...] Read more.
Differences between conventional sonar and Multiple-Input Multiple-Output (MIMO) sonar systems arise in achieving high angular and range resolution. MIMO sonar uses Matched Filtering (MF) with well-correlated transmitted signals to enhance spatial resolution by obtaining virtual arrays. However, imperfect correlation characteristics yield high sidelobe values, which hinder accurate target localization in underwater imagery. To address this, a Compressed Sensing (CS) method is proposed by reconstructing echo signals to suppress correlation noise between orthogonal waveforms. A shifted dictionary matrix and a deterministic Discrete Fourier Transform (DFT) measurement matrix are used to multiply received echo signals to yield compressed measurements. A sparse recovery algorithm is applied to optimize signal reconstruction before joint transmit–receive beamforming forms a 2D sonar image in the angle-range domain. Numerical simulations and lake experimental results confirm the effectiveness of the proposed method, by obtaining a lower sidelobe sonar image under sub-Nyquist sampling rates as compared with other approaches. Full article
(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing II)
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