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Electronics
Special Issues
Low-Frequency Underwater Acoustic Signal Processing and Applications
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Low-Frequency Underwater Acoustic Signal Processing and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 January 2026 | Viewed by 1018

Special Issue Editors

Prof. Dr. Yong Wang

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: beamforming; direction-of-arrival (DOA) estimation; target detection and recognition; applications of related methodologies
Prof. Dr. Peng Xiao

E-Mail Website
Guest Editor
School of Ocean Engineering and Technology, Sun Yat-sen University, Zhuhai 519082, China
Interests: modeling and analysis of underwater acoustics; object detection and localization in the ocean; deep learning for acoustic computations
Prof. Dr. Long Yang

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: off-grid direction-of-arrival estimation; array signal processing; adaptive tracking; self-calibration

Special Issue Information

Dear Colleagues,

Low-frequency underwater acoustic signal processing plays a pivotal role in marine exploration, environmental monitoring, and underwater communication systems. Due to the unique propagation characteristics of low-frequency signals (typically below 1 kHz) in water—such as reduced attenuation and long-range transmission—they are indispensable for applications like underwater target detection, seismic activity monitoring, marine biodiversity studies, and underwater vehicle navigation. However, the complex underwater environment, characterized by ambient noise, multipath interference, and dynamic channel variations, poses significant challenges to signal acquisition, analysis, and interpretation.

Key research areas focus on enhancing signal-to-noise ratio (SNR) in the low-frequency range through adaptive filtering and beamforming techniques, developing robust algorithms for feature extraction and recognition in noisy environments, and improving localization accuracy via time/frequency/space processing. Additionally, artificial intelligence-based methods are increasingly demonstrating critical importance in the low-frequency range of underwater acoustic signal processing. These advancements strengthen global capabilities in safeguarding maritime security and sustainable ocean development.

The aim of this Special Issue of Electronics is to present state-of-the-art investigations in various methods for low-frequency underwater acoustic signal processing and applications. We invite researchers to contribute original high-quality articles, as well as sophisticated review articles.

Prof. Dr. Yong Wang
Prof. Dr. Peng Xiao
Prof. Dr. Long Yang
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. Electronics 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 2400 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.

Keywords

  • underwater sound propagation
  • ambient noise, including sources, monitoring and long-term trends
  • arctic acoustics
  • acoustic studies of marine mammals
  • geoacoustics
  • nonlinear underwater acoustics
  • ocean tomography
  • sonar performance measurements and modeling
  • target detection, localization, tracking, and recognition
  • spectral estimation, beamforming, and localization
  • matched-field processing
  • adaptive array processing
  • distributed array processing
  • transducer and array technology
  • vector sensors and new low-frequency acoustic sensors
  • artificial intelligence-based processing approaches
  • information fusion-based methods
  • underwater communication
  • underwater bioacoustic monitoring

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Published Papers (2 papers)

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Research

31 pages, 17812 KiB  
Article
Deep Learning-Based Source Localization with Interference Striation of a Towed Horizontal Line Array
by Zhengchao Huang, Yanfa Deng, Peng Qian, Zhenglin Li and Peng Xiao
Electronics 2025, 14(15), 3053; https://doi.org/10.3390/electronics14153053 - 30 Jul 2025
Viewed by 266
Abstract
The aperture of the towed horizontal line array is limited and the received signal is unstable in a complex ocean environment, making it difficult to distinguish the location of the sound source. To address this challenge, this paper presents a MoELocNet (Mixture of [...] Read more.
The aperture of the towed horizontal line array is limited and the received signal is unstable in a complex ocean environment, making it difficult to distinguish the location of the sound source. To address this challenge, this paper presents a MoELocNet (Mixture of Experts Localization Network) for deep-sea sound source localization, leveraging interference structures in range-frequency domain signals from a towed horizontal line array. Unlike traditional correlation-based methods constrained by time-varying ocean environments and low signal-to-noise ratios, the model employs multi-expert and multi-task learning to extract interference periods from single-frame data, enabling robust estimation of source range and depth. Simulation results demonstrate its superior performance in the deep-sea shadow zone, achieving a range localization error of 0.029 km and a depth error of 0.072 m. The method exhibits strong noise robustness and delivers satisfactory results across diverse deep-sea zones, with optimal performance in shadow zones and secondary effectiveness in the direct arrival zone. Full article
(This article belongs to the Special Issue Low-Frequency Underwater Acoustic Signal Processing and Applications)
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18 pages, 2199 KiB  
Article
An Enhanced Approach for Sound Speed Profiles Inversion Using Remote Sensing Data: Sample Clustering and Physical Regression
by Zixuan Zhang, Ke Qu and Zhanglong Li
Electronics 2025, 14(14), 2822; https://doi.org/10.3390/electronics14142822 - 14 Jul 2025
Viewed by 271
Abstract
Sound speed profile (SSP) inversion based on remote sensing parameters allows for the acquisition of global quasi-real-time SSPs without the need for on-site measurements, thereby fulfilling the requirements of many acoustic applications. This study makes two enhancements to the single empirical orthogonal function [...] Read more.
Sound speed profile (SSP) inversion based on remote sensing parameters allows for the acquisition of global quasi-real-time SSPs without the need for on-site measurements, thereby fulfilling the requirements of many acoustic applications. This study makes two enhancements to the single empirical orthogonal function regression (SEOF-R) method. First, the k-means clustering algorithm is utilized to cluster SSP samples, ensuring the consistency of perturbation modes in the physical regression. Second, baroclinic modes are employed to derive a novel SSP basis function, named the ocean mode basis, which accurately characterizes the inversion relationship. Validation experiments using data from the South China Sea yield promising results. Compared with the SEOF-R method, the reconstruction error of the improved approach is reduced by 27%, with an average reconstruction error of 1.73 m/s. The average prediction transmission loss error decreases by 70%, reaching 1.29 dB within 50 km. The grid-free processing and low sample dependence of the proposed method further enhance the applicability and accuracy of remote sensing-based SSP inversion. Full article
(This article belongs to the Special Issue Low-Frequency Underwater Acoustic Signal Processing and Applications)
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