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Electronics
Special Issues
Applications and Challenges in Sonar Signal Processing
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Applications and Challenges in Sonar Signal Processing

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

Deadline for manuscript submissions: closed (25 February 2024) | Viewed by 5461

Special Issue Editors

Dr. Feiyun Wu

E-Mail
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: compressed sensing; adaptive signal processing; channel estimation and equalizer design
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Feng Tong

E-Mail Website
Guest Editor
College of Ocean and Earth Sciences, Xiamen University, Xiamen 361005, China
Interests: underwater acoustic communication and network; UWA channel estimation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yougan Chen

E-Mail Website
Guest Editor
College of Ocean and Earth Sciences, Xiamen University, Xiamen 361005, China
Interests: underwater acoustic communications; cooperative communications and networking; application of AI in underwater acoustics

Special Issue Information

Dear Colleagues,

Sonar signal processing plays a crucial role in various underwater applications, ranging from marine exploration and navigation to defense and environmental monitoring. This Special Issue aims to highlight the latest advancements, applications, and challenges in sonar signal processing techniques and methodologies.

The aim of this Special Issue of Electronics is to invite original research articles, review papers, and case studies that address the following topics:

  • Sonar signal processing algorithms: Novel algorithms for sonar data acquisition, processing, and analysis, including beamforming, target detection, classification, imaging, tracking, communications, and localization;
  • Advanced signal processing techniques: Innovative approaches in signal processing to enhance the performance of sonar systems, such as adaptive filtering, time–frequency analysis, compressive sensing, machine learning, and deep learning;
  • Sonar applications in marine exploration: Studies showcasing the use of sonar signal processing in underwater mapping, underwater archaeological surveys, marine resource exploration, and underwater robotics;
  • Sonar systems in defense and security: Contributions to sonar technologies for naval warfare, submarine detection and tracking, anti-submarine warfare, mine detection and classification, and underwater surveillance;
  • Sonar signal processing for environmental monitoring: Research focusing on the application of sonar systems for assessing marine ecosystems, studying marine life, underwater noise analysis, and pollution detection in underwater environments;
  • Challenges and future directions: Discussions on the emerging challenges, limitations, and future trends in sonar signal processing, including issues related to noise, interference, underwater communications, data fusion, and integration with other sensing modalities;

Researchers, engineers, and practitioners from academia, industry, and government institutions are encouraged to contribute to this Special Issue. The aim is to provide a comprehensive overview of the latest developments and applications in sonar signal processing and to address the key challenges in this field.

Submission guidelines and additional information will be available on the journal's website. We welcome high-quality submissions that present original research and contribute to the advancement of sonar signal processing techniques and their applications.

Technical Program Committee Members:

Name: Prof. Dr. Longyu Jiang
Email: jly@seu.edu.cn
Affiliation: School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Research Interests: underwater acoustic signal and image processing; artificial intelligence and big data

Name: Dr. Lianyou Jing
Email: lyjing@nwpu.edu.cn
Affiliation: Ocean Institute, Northwestern Polytechnical University, Xi'an 215400, China
Research Interests: underwater acoustic channel estimation; channel equalization; novel underwater acoustic communication methods; adaptive underwater acoustic communication

Dr. Feiyun Wu
Prof. Dr. Feng Tong
Prof. Dr. Yougan Chen
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

  • sonar signal processing
  • underwater acoustics
  • beamforming
  • target detection
  • target classification
  • imaging sonar
  • sonar tracking
  • sonar localization
  • adaptive filtering
  • time-frequency analysis
  • compressive sensing
  • machine learning in sonar
  • deep learning in sonar
  • underwater mapping
  • underwater archaeology
  • marine resource exploration
  • underwater robotics

Published Papers (6 papers)

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Research

14 pages, 5227 KiB  
Article
Acoustic Target Strength of Thornfish (Terapon jarbua) Based on the Kirchhoff-Ray Mode Model
by Bin Li, Jiahao Liu, Xiujing Gao, Hongwu Huang, Fang Wang and Zhuoya Huang
Electronics 2024, 13(7), 1279; https://doi.org/10.3390/electronics13071279 - 29 Mar 2024
Viewed by 364
Abstract
Thornfish (Terapon jarbua) is a significantly commercial species inhabiting the shallow coastal waters of the Indo-Pacific Ocean. To achieve effective underwater acoustic (UWA) monitoring on the abundance and population dynamics of this species, the comprehensive target strength (TS) characteristics should be [...] Read more.
Thornfish (Terapon jarbua) is a significantly commercial species inhabiting the shallow coastal waters of the Indo-Pacific Ocean. To achieve effective underwater acoustic (UWA) monitoring on the abundance and population dynamics of this species, the comprehensive target strength (TS) characteristics should be investigated and understood. In this study, the Kirchhoff-ray mode (KRM) model was adopted to evaluate and analyze the acoustic TS of T. jarbua and its variations with the sound wave frequency, pitch angle distributions as well as morphological characteristics in the South China Sea. A total of 19 samples were captured and evaluated at four types of frequencies of 38 kHz, 70 kHz, 120 kHz, and 200 kHz. The results demonstrated that the TS of T. jarbua varied with the pitch angle shifts, and the number of secondary TS peaks increased as the increasing frequency accordingly. Two classic pitch angle distributions that included N[−5°, 15°] and N[0°, 10°] were adopted to calculate the average TS of T. jarbua. The fitted TS-L regression formulations and the standard b20 form equations were determined at different pitch angle distributions as well as frequencies. These results could support the accurate and reliable UWA abundance estimation in the South China Sea to facilitate a better understanding of the abundance and population dynamics of T. jarbua. Full article
(This article belongs to the Special Issue Applications and Challenges in Sonar Signal Processing)
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17 pages, 9408 KiB  
Article
A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm
by Boyu Zhu, Biao Wang, Banggui Cai and Yunan Zhu
Electronics 2024, 13(4), 758; https://doi.org/10.3390/electronics13040758 - 14 Feb 2024
Viewed by 515
Abstract
Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean [...] Read more.
Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm. Full article
(This article belongs to the Special Issue Applications and Challenges in Sonar Signal Processing)
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19 pages, 11050 KiB  
Article
Analysis of High-Frequency Communication Channel Characteristics in a Typical Deep-Sea Incomplete Sound Channel
by Yunfei Li, Ning Jia, Jianchun Huang, Ruigang Han, Zhongyuan Guo and Shengming Guo
Electronics 2023, 12(22), 4562; https://doi.org/10.3390/electronics12224562 - 07 Nov 2023
Viewed by 761
Abstract
Deep-sea acoustic communication has attracted widespread attention in recent years. Research on deep-sea acoustic communication channel characteristics is essential for the development and system design of deep-sea acoustic communication technologies. However, the structural and spatiotemporal characteristics of deep-sea high-frequency acoustic communication channels (unlike [...] Read more.
Deep-sea acoustic communication has attracted widespread attention in recent years. Research on deep-sea acoustic communication channel characteristics is essential for the development and system design of deep-sea acoustic communication technologies. However, the structural and spatiotemporal characteristics of deep-sea high-frequency acoustic communication channels (unlike those of shallow-water acoustic communication channels) are poorly understood. Based on a channel measurement experiment in a typical deep-sea incomplete sound channel environment in the South China Sea, this paper analyzes the structural and spatiotemporal characteristics of high-frequency underwater acoustic channels in the direct-arrival and shadow zones. The channel multipath vertical structure, amplitudes of different path clusters, root mean square (RMS) delay spread, and channel temporal coherence are investigated at different depths. The randomness of different path clusters is quantified as the coefficient of variation. The channels in the shadow zone are characterized by a complex multi-path structure, high RMS delay spread, and low temporal coherence. On the contrary, the channels of the direct-arrival zone show multi-path convergence, low RMS delay spread, and high temporal coherence. Understanding the dynamic changes in these parameters can guide the statistical modeling of channels and the design of communication algorithms in different zones in deep-sea high-frequency acoustic communication scenarios. Full article
(This article belongs to the Special Issue Applications and Challenges in Sonar Signal Processing)
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16 pages, 9720 KiB  
Article
Short-Block-Length Low-Density Parity-Check Codes-Based Underwater Acoustic Spread-Spectrum Communication System
by Zichen Zhao and Zongxin Sun
Electronics 2023, 12(18), 3884; https://doi.org/10.3390/electronics12183884 - 14 Sep 2023
Cited by 1 | Viewed by 783
Abstract
Low-density parity-check (LDPC) codes are commonly used in communication systems to improve the system performance, but LDPC codes takes too long for decoding, making communication inefficient and unsuitable for short-frame data transmission methods. In underwater acoustic channels, spread-spectrum communication becomes an effective way [...] Read more.
Low-density parity-check (LDPC) codes are commonly used in communication systems to improve the system performance, but LDPC codes takes too long for decoding, making communication inefficient and unsuitable for short-frame data transmission methods. In underwater acoustic channels, spread-spectrum communication becomes an effective way to realize long-distance communication. This paper combines short-block LDPC codes with a direct sequence spread spectrum and soft spread spectrum in underwater acoustic communication, addressing the problem of the inapplicability of conventional LDPC codes. The applicability of the proposed method in this paper is verified through simulation tests and pool experiments. The results indicate that the proposed communication system achieves lower bit error rates compared to the classical coding methods used in underwater acoustic spread-spectrum communication systems under the same channel conditions. Full article
(This article belongs to the Special Issue Applications and Challenges in Sonar Signal Processing)
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12 pages, 3166 KiB  
Article
Robust Adaptive Beamforming Algorithm Based on Complex Gauss–Legendre Integral
by Lin Cui, Kai Xue, Boyan Wang and Yuanbang Zhang
Electronics 2023, 12(18), 3794; https://doi.org/10.3390/electronics12183794 - 07 Sep 2023
Viewed by 543
Abstract
This study addresses the poor robustness of the current beamforming algorithm for covariance matrix reconstruction and the high computational complexity in the covariance matrix reconstruction process. A robust beamforming algorithm based on the Complex Gauss–Legendre integral is proposed. The method firstly partitions the [...] Read more.
This study addresses the poor robustness of the current beamforming algorithm for covariance matrix reconstruction and the high computational complexity in the covariance matrix reconstruction process. A robust beamforming algorithm based on the Complex Gauss–Legendre integral is proposed. The method firstly partitions the neighborhood of the interference signal and constructs the interference signal space using the complex Gauss-Legendre integral, then projects the received signal into the interference signal space to filter out the desired signal and complete the reconstruction of the interference noise covariance matrix, before finally correcting the steering vector mismatch using the improved optimal steering vector estimation method. The simulation results show that the method has good robustness and a low sidelobe in the presence of the steering vector mismatch and the presence of array perturbation. Compared with the previous works, the proposed CGL-ISV method provides a better beamforming performance. Full article
(This article belongs to the Special Issue Applications and Challenges in Sonar Signal Processing)
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12 pages, 4139 KiB  
Article
Research on an Underwater Object Detection Network Based on Dual-Branch Feature Extraction
by Xiao Chen, Mujiahui Yuan, Chenye Fan, Xingwu Chen, Yaan Li and Haiyan Wang
Electronics 2023, 12(16), 3413; https://doi.org/10.3390/electronics12163413 - 11 Aug 2023
Cited by 2 | Viewed by 1308
Abstract
Underwater object detection is challenging in computer vision research due to the complex underwater environment, poor image quality, and varying target scales, making it difficult for existing object detection networks to achieve high accuracy in underwater tasks. To address the issues of limited [...] Read more.
Underwater object detection is challenging in computer vision research due to the complex underwater environment, poor image quality, and varying target scales, making it difficult for existing object detection networks to achieve high accuracy in underwater tasks. To address the issues of limited data and multi-scale targets in underwater detection, we propose a Dual-Branch Underwater Object Detection Network (DB-UODN) based on dual-branch feature extraction. In the feature extraction stage, we design a dual-branch structure by combining the You Only Look Once (YOLO) v7 backbone with the Enhanced Channel and Dilated Block (ECDB). It allows for the extraction and complementation of multi-scale features, which enable the model to learn both global and local information and enhance its perception of multi-scale features in underwater targets. Furthermore, we employ the DSPACSPC structure to replace the SPPCSPC structure in YOLOv7. The DSPACSPC structure utilizes atrous convolutions with different dilation rates to capture contextual information at various scales, compensating for potential information loss caused by pooling operations. Additionally, we utilize a dense connection structure to facilitate feature reuse and enhance the network’s representation and generalization capabilities. Experimental results demonstrate that the proposed DB-UODN outperforms the most commonly used object detection networks in underwater scenarios. On the URPC2020 dataset, the network achieves an average detection accuracy of 87.36%. Full article
(This article belongs to the Special Issue Applications and Challenges in Sonar Signal Processing)
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