Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications

Topic Information

Dear Colleagues,

Underwater signal processing and communication technologies play a crucial role in a wide range of applications, including marine exploration, environmental monitoring, underwater resource mapping, disaster response, and defense operations. The complex and dynamic nature of the underwater environment presents significant challenges, such as signal attenuation, multipath propagation, and the absence of GPS, necessitating advanced signal processing techniques and robust communication systems.

Key challenges and research directions in this field include the following:

• Accurate Positioning and Navigation: In GPS-denied underwater environments, technologies such as acoustic localization, inertial navigation systems (INSs), simultaneous localization and mapping (SLAM), and SONAR-based navigation are essential for precise underwater positioning. 
• Reliable Communication: Underwater communication relies on acoustic, optical, and electromagnetic methods, each with its own trade-offs. Innovations in modulation techniques, adaptive transmission, and hybrid communication strategies are critical for improving data transmission reliability. 
• Real-Time Data Processing: Autonomous underwater systems, including unmanned underwater vehicles (UUVs) and sensor networks, require the real-time processing of sensor data for adaptive decision making, target recognition, and environmental modeling. 
• Noise Reduction and Signal Enhancement: Underwater environments introduce challenges such as multipath interference, Doppler effects, and background noise. Advanced filtering, beamforming, and machine learning-based noise reduction techniques are vital for improving signal clarity. 
• Energy-Efficient Systems: Long-duration underwater operations demand energy-efficient signal processing and communication methods to maximize operational lifetime, particularly for deep-sea exploration and long-term environmental monitoring. 
• SONAR-Based Sensing and Imaging: SONAR technologies, including multibeam SONAR and synthetic aperture SONAR (SAS), are widely used for underwater mapping, object detection, and marine habitat assessment. Advanced signal processing techniques enhance SONAR resolution, reduce interference, and improve target classification. 
• AI and Machine Learning for Underwater Systems: The integration of AI into underwater signal processing enables intelligent decision making, anomaly detection, and predictive maintenance for various applications, from autonomous navigation to underwater surveillance.

This topic aims to explore cutting-edge innovations in underwater signal processing and communication, bringing together experts from diverse fields such as signal processing, telecommunications, robotics, oceanography, and environmental science. By addressing these challenges, advancements in this field will contribute to improved underwater exploration, scientific research, industrial applications, and defense capabilities.

Prof. Dr. Jaehak Chung
Dr. Hojun Lee
Dr. Yongcheol Kim
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Journal of Marine Science and Engineering jmse	2.8	5.0	2013	15.6 Days	CHF 2600	Submit
Signals signals	2.6	4.6	2020	22.9 Days	CHF 1200	Submit
Telecom telecom	2.4	5.4	2020	26.3 Days	CHF 1200	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit

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Published Papers (9 papers)

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19 pages, 5701 KB  

Open AccessArticle

Research on Fault Diagnosis Method for Autonomous Underwater Vehicles Based on Improved LSTM Under Data Missing Conditions

by Lingyan Dong and Yan Huo

Appl. Sci. 2025, 15(21), 11570; https://doi.org/10.3390/app152111570 - 29 Oct 2025

Viewed by 144

Abstract

Fault diagnosis for Autonomous underwater Vehicle (AUVs) is a key technology for ensuring the safety of AUVs and an important skill for enabling them to autonomously perform tasks underwater for long periods. The effectiveness of current diagnostic methods is affected by the reliability [...] Read more.

Fault diagnosis for Autonomous underwater Vehicle (AUVs) is a key technology for ensuring the safety of AUVs and an important skill for enabling them to autonomously perform tasks underwater for long periods. The effectiveness of current diagnostic methods is affected by the reliability of expert knowledge and the accuracy of model establishment. In addition, some data-driven diagnostic methods lack robustness. Unlike traditional model-based fault diagnosis methods, this paper proposes a fault diagnosis method for AUVs based on the LSTM (Long Short-Term Memory) algorithm. LSTM is good at processing time series data and can learn complex temporal patterns. Therefore, the LSTM model is used to learn the mapping of state data to its corresponding fault types. The underwater environment in which AUVs work is complex and ever-changing, and packet loss may occur during data transmission, resulting in partial loss of online data. To address this issue, this paper fills in missing values during the feature processing stage and then uses a BiLSTM-Attention-MiniLoss algorithm to enhance the robustness of the diagnostic model. Finally, the fault diagnosis accuracy of the original LSTM and the BiLSTM-Attention-MiniLoss was compared based on an open-source dataset under different degrees of data loss. The experimental results showed that the fault diagnosis methods for AUV based on LSTM and the BiLSTM-Attention-MiniLoss could predict the type of fault based on the navigation status data of the AUV, with BiLSTM-Attention-MiniLoss performing better. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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29 pages, 7170 KB  

Open AccessArticle

Two Non-Learning Systems for Profile-Extraction in Images Acquired from a near Infrared Camera, Underwater Environment, and Low-Light Condition

by Tianyu Sun, Jingmei Xu, Zongan Li and Ye Wu

Appl. Sci. 2025, 15(20), 11289; https://doi.org/10.3390/app152011289 - 21 Oct 2025

Viewed by 265

Abstract

The images acquired from near infrared cameras can contain thermal noise, which degrades the quality of the images. The quality of the images obtained from underwater environments suffer from the complex hydrological environment. All these issues make the profile-extraction in these images a [...] Read more.

The images acquired from near infrared cameras can contain thermal noise, which degrades the quality of the images. The quality of the images obtained from underwater environments suffer from the complex hydrological environment. All these issues make the profile-extraction in these images a difficult task. In this work, two non-learning systems are built for making filters by using wavelets transform combined with simple functions. They can be shown to extract profiles in the images acquired from the near infrared camera and underwater environment. Furthermore, they are useful for low-light image enhancement, edge/array detection, and image fusion. The increase in the measurement by entropy can be found by enhancing the scale of the filters. When processing the near infrared images, the values of running time, the memory usage, Signal-to-Noise Ratio (SNR), and Peak Signal-to-Noise Ratio (PSNR) are generally smaller in the operators of Canny, Roberts, Log, Sobel, and Prewitt than those in the Atanh filter and Sech filter. When processing the underwater images, the values of running time, the memory usage, SNR, and PSNR are generally smaller in Sobel operator than those in the Atanh filter and Sech filter. When processing the low-light images, it can be seen that the Atanh filter obtains the highest values of the running time and the memory usage compared to the filter based on the Retinex model, the Sech filter, and a matched filter. Our designed filters require little computational resources comparing to learning-based ones and hold the merits of being multifunctional, which may be useful for advanced imaging in the field of bio-medical engineering. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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25 pages, 10667 KB  

Open AccessArticle

Adaptive Exposure Optimization for Underwater Optical Camera Communication via Multimodal Feature Learning and Real-to-Sim Channel Emulation

by Jiongnan Lou, Xun Zhang, Haifei Shen, Yiqian Qian, Zhan Wang, Hongda Chen, Zefeng Wang and Lianxin Hu

Sensors 2025, 25(20), 6436; https://doi.org/10.3390/s25206436 - 17 Oct 2025

Viewed by 519

Abstract

Underwater Optical Camera Communication (UOCC) has emerged as a promising paradigm for short-range, high-bandwidth, and secure data exchange in autonomous underwater vehicles (AUVs). UOCC performance strongly depends on exposure time and International Standards Organization (ISO) sensitivity—two parameters that govern photon capture, contrast, and [...] Read more.

Underwater Optical Camera Communication (UOCC) has emerged as a promising paradigm for short-range, high-bandwidth, and secure data exchange in autonomous underwater vehicles (AUVs). UOCC performance strongly depends on exposure time and International Standards Organization (ISO) sensitivity—two parameters that govern photon capture, contrast, and bit detection fidelity. However, optical propagation in aquatic environments is highly susceptible to turbidity, scattering, and illumination variability, which severely degrade image clarity and signal-to-noise ratio (SNR). Conventional systems with fixed imaging settings cannot adapt to time-varying conditions, limiting communication reliability. While validating the feasibility of deep learning for exposure prediction, this baseline lacked environmental awareness and generalization to dynamic scenarios. To overcome these limitations, we introduce a Real-to-Sim-to-Deployment framework that couples a physically calibrated emulation platform with a Hybrid CNN-MLP Model (HCMM). By fusing optical images, environmental states, and camera configurations, the HCMM achieves substantially improved parameter prediction accuracy, reducing RMSE to 0.23–0.33. When deployed on embedded hardware, it enables real-time adaptive reconfiguration and delivers up to 8.5 dB SNR gain, surpassing both static-parameter systems and the prior CNN baseline. These results demonstrate that environment-aware multimodal learning, supported by reproducible optical channel emulation, provides a scalable and robust solution for practical UOCC deployment in positioning, inspection, and laser-based underwater communication. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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37 pages, 7185 KB  

Open AccessArticle

Position Calibration of Shallow-Sea Hydrophone Arrays in Reverberant Environments

by Changjing Xiong, Bo Yang, Wei Wang, Yeyao Liu, Tianli Liu, Dahai Yu and Chuanhe Li

J. Mar. Sci. Eng. 2025, 13(10), 1922; https://doi.org/10.3390/jmse13101922 - 7 Oct 2025

Viewed by 306

Abstract

To address the problem of shallow-sea hydrophone calibration, this paper proposes a shallow-sea hydrophone calibration algorithm for the horizontal and depth directions, respectively. In the horizontal direction, a calibration method combining an improved Particle Swarm Optimization (PSO) algorithm and the Time Difference Of [...] Read more.

To address the problem of shallow-sea hydrophone calibration, this paper proposes a shallow-sea hydrophone calibration algorithm for the horizontal and depth directions, respectively. In the horizontal direction, a calibration method combining an improved Particle Swarm Optimization (PSO) algorithm and the Time Difference Of Arrival (TDOA) algorithm is proposed. In the depth direction, a depth calibration formula using the time delay difference between Non-Line-of-Sight (NLOS) waves and Line-of-Sight (LOS) waves is put forward. By combining this with the proposed PSO algorithm, the PSO NLOS–LOS depth correction algorithm is obtained. The specific position of the hydrophone is determined by combining the algorithms for horizontal direction and depth. The advantages of the proposed algorithms are verified through simulations and experiments. Simulations show that in the horizontal direction, the proposed algorithm can reduce the average calibration error under different hydrophone array radii to 0.8690 m. In the depth direction, the specific propagation delay is unknown. Compared with the traditional depth calculation method, which requires the specific propagation delay to be known, the algorithm proposed in this paper can reduce the impact on depth calculation caused by delay deviation due to sound ray refraction; in addition, it provides stronger robustness and more accurate depth calibration in shallow sea environments. The new method shows significant improvement in the depth calculation process compared with the traditional algorithm, especially in terms of fault tolerance for errors in the horizontal direction. Experiments show that by combining the calibration algorithms proposed in this paper, the positioning accuracy of the hydrophone array is significantly improved and the average positioning error of the hydrophone array is reduced to within 12 m. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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14 pages, 3118 KB  

Open AccessArticle

Reconstruction Modeling and Validation of Brown Croaker (Miichthys miiuy) Vocalizations Using Wavelet-Based Inversion and Deep Learning

by Sunhyo Kim, Jongwook Choi, Bum-Kyu Kim, Hansoo Kim, Donhyug Kang, Jee Woong Choi, Young Geul Yoon and Sungho Cho

Sensors 2025, 25(19), 6178; https://doi.org/10.3390/s25196178 - 6 Oct 2025

Viewed by 426

Abstract

Fish species’ biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (Miichthys miiuy), hampers data-driven bioacoustic methodology development. In this [...] Read more.

Fish species’ biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (Miichthys miiuy), hampers data-driven bioacoustic methodology development. In this study, we present a framework for reconstructing brown croaker vocalizations by integrating fk14 wavelet synthesis, PSO-based parameter optimization (with an objective combining correlation and normalized MSE), and deep learning-based validation. Sensitivity analysis using a normalized Bartlett processor identified delay and scale (length) as the most critical parameters, defining valid ranges that maintained waveform similarity above 98%. The reconstructed signals matched measured calls in both time and frequency domains, replicating single-pulse morphology, inter-pulse interval (IPI) distributions, and energy spectral density. Validation with a ResNet-18-based Siamese network produced near-unity cosine similarity (~0.9996) between measured and reconstructed signals. Statistical analyses (95% confidence intervals; residual errors) confirmed faithful preservation of SPL values and minor, biologically plausible IPI variations. Under noisy conditions, similarity decreased as SNR dropped, indicating that environmental noise affects reconstruction fidelity. These results demonstrate that the proposed framework can reliably generate acoustically realistic and morphologically consistent fish vocalizations, even under data-limited scenarios. The methodology holds promise for dataset augmentation, PAM applications, and species-specific call simulation. Future work will extend this framework by using reconstructed signals to train generative models (e.g., GANs, WaveNet), enabling scalable synthesis and supporting real-time adaptive modeling in field monitoring. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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21 pages, 9585 KB  

Open AccessArticle

Multi-Mode Joint Equalization Scheme for Low Frequency and Long Range Shallow Water Communications

by Shuang Xiao, Yaqi Zhang, Bin Liu, Hongyu Cui and Dazhi Gao

J. Mar. Sci. Eng. 2025, 13(8), 1587; https://doi.org/10.3390/jmse13081587 - 19 Aug 2025

Viewed by 421

Abstract

To improve the spatial processing performance in the low frequency and long range shallow water communication system, a multi-mode joint equalization scheme is proposed, which combines modal depth function estimation, mode filtering, and multi-input equalization. This method first estimates the modal depth function [...] Read more.

To improve the spatial processing performance in the low frequency and long range shallow water communication system, a multi-mode joint equalization scheme is proposed, which combines modal depth function estimation, mode filtering, and multi-input equalization. This method first estimates the modal depth function of the effective modes by Singular Value Decomposition (SVD) of Cross Spectral Density Matrix (CDSM), then separates the influence of each mode on the continuous-time signal by the vertical array mode filtering without any prior information. After these pre-processings, the separated signal is only affected by the single channel mode, and the output Signal-to-Noise Ratio (SNR) is enhanced, and channel delay spread is reduced simultaneously. All the separated parts are then sent to a multi-input equalizer to compensate for the channel fading between different modes.Simulation results verify that compared with single channel equalization after beamforming and multichannel equalization, the proposed multi-mode joint equalization can obtain 3 dB and 6 dB gain, respectively. Experimental results also show that the proposed equalization can achieve lower Bit Error Rate (BER) and higher output SNR. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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18 pages, 12793 KB  

Open AccessArticle

A Mainlobe Interference Suppression Method for Small Hydrophone Arrays

by Wenbo Wang, Ye Li, Luwen Meng, Tongsheng Shen and Dexin Zhao

J. Mar. Sci. Eng. 2025, 13(7), 1348; https://doi.org/10.3390/jmse13071348 - 16 Jul 2025

Viewed by 400

Abstract

In order to solve the problem of mainlobe interference in small hydroacoustic array signal processing, this paper proposes a beamforming method based on the high-resolution direction of arrival (DOA) estimation and interference coherence matrix (ICM) reconstruction. The DOA estimation is first performed using [...] Read more.

In order to solve the problem of mainlobe interference in small hydroacoustic array signal processing, this paper proposes a beamforming method based on the high-resolution direction of arrival (DOA) estimation and interference coherence matrix (ICM) reconstruction. The DOA estimation is first performed using an improved sparse iterative covariance-based (SPICE) method, unaffected by the coherent signal, and it can provide highly accurate DOA estimation for multiple targets. The fitted signal energy distribution obtained from the SPICE is then utilized for the reconstruction of the signal coherence matrix. The reconstructed ICM matrix is used to construct a blocking masking matrix and an eigen-projection matrix to suppress the mainlobe interference signal. Compared with existing methods, the method in this paper possesses better mainlobe interference suppression ability. Within the mainlobe interference interval angle of 3° to 13.5° from the signal of interest (SOI) based on eight-element uniform linear arrays, the method in this paper can enhance the signal-to-interference ratio (SIR) by about 15.59 dB on average compared with the interference-free suppression of conventional beamforming (CBF) and outperforms the other interference suppression methods simultaneously. Simulations and experiments demonstrate the effectiveness of this method in mainlobe interference scenarios. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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16 pages, 2292 KB  

Open AccessArticle

Passive Synthetic Aperture for Direction-of-Arrival Estimation Using an Underwater Glider with a Single Hydrophone

by Yueming Ma, Jie Sun, Shuo Li, Tianze Hu, Shilong Li and Yuexing Zhang

J. Mar. Sci. Eng. 2025, 13(7), 1322; https://doi.org/10.3390/jmse13071322 - 10 Jul 2025

Viewed by 602

Abstract

This paper addresses the aperture limitation problem faced by array-equipped underwater gliders (UGs) in direction-of-arrival (DOA) estimation. A passive synthetic aperture (PSA) method for DOA estimation using a single hydrophone mounted on a UG is proposed. This method uses the motion of the [...] Read more.

This paper addresses the aperture limitation problem faced by array-equipped underwater gliders (UGs) in direction-of-arrival (DOA) estimation. A passive synthetic aperture (PSA) method for DOA estimation using a single hydrophone mounted on a UG is proposed. This method uses the motion of the UG to synthesize a linear array whose elements are positioned to acquire the target signal, thereby increasing the array aperture. The dead-reckoning method is used to determine the underwater trajectory of the UG, and the UG’s trajectory was corrected by the UG motion parameters, from which the array shape was adjusted accordingly and the position of the array elements was corrected. Additionally, array distortion caused by movement offsets due to ocean currents underwent linearization, reducing computational complexity. To validate the proposed method, a sea trial was conducted in the South China Sea using the Haiyi 1000 UG equipped with a hydrophone, and its effectiveness was demonstrated through the processing of the collected data. The performance of DOA estimation prior to and following UG trajectory correction was compared to evaluate the impact of ocean currents on target DOA estimation accuracy. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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19 pages, 2700 KB  

Open AccessArticle

Underwater Low-Frequency Magnetic Field Detection Based on Rao’s Sliding Threshold Method

by Yi Li and Jiawei Zhang

Sensors 2025, 25(11), 3364; https://doi.org/10.3390/s25113364 - 27 May 2025

Viewed by 866

Abstract

This paper proposes a joint time–frequency analysis method that combines Rao detector with dynamic sliding thresholds to enhance the detection performance of electric source axial frequency magnetic field signals. For each signal-to-noise ratio (SNR) point, 1000 Monte Carlo simulations were independently conducted, with [...] Read more.

This paper proposes a joint time–frequency analysis method that combines Rao detector with dynamic sliding thresholds to enhance the detection performance of electric source axial frequency magnetic field signals. For each signal-to-noise ratio (SNR) point, 1000 Monte Carlo simulations were independently conducted, with SNR ranging from 15 dB to −30 dB. The results show that the proposed method maintains high detection rates even at extremely low SNRs, achieving about 90% detection probability at −13 dB, significantly outperforming traditional energy detectors (with a threshold of 2 dB). Under conditions where the detection probability is ≥90% and the false alarm probability is 10⁻³, the SNR threshold for the Rao detector is reduced by 15 dB compared to energy detectors, greatly improving detection performance. Even at lower SNRs (−30 dB), the Rao detector still maintains a certain detection rate, while the detection rate of energy detectors rapidly drops to zero. Further analysis of the impact of different frequencies (1–5 Hz) and CPA distances (45–80 cm) on performance verifies the algorithm’s robustness and practicality in complex non-Gaussian noise environments. This method provides an effective technical solution for low SNR detection of ship axial frequency magnetic fields and has good potential for practical application. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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