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

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Journal of Marine Science and Engineering jmse	3.2	5.6	2013	15 Days	CHF 2600	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Signals signals	2.9	5.9	2020	28.6 Days	CHF 1200	Submit
Telecom telecom	2.8	5.2	2020	22.8 Days	CHF 1400	Submit

24 pages, 4826 KB

Open AccessArticle

Analysis of the Adaptability and Application of Matched-Field Processors for Stationary and Maneuvering Targets in Shallow Water

by Zikun Meng, Wen Zhang, Jian Shi, Shuo Liu and Qiankun Yu

J. Mar. Sci. Eng. 2026, 14(14), 1259; https://doi.org/10.3390/jmse14141259 - 8 Jul 2026

Viewed by 182

Abstract

Passive acoustic localization in complex shallow waters requires algorithms tailored to specific operational constraints. This paper investigates the adaptability, computational efficiency, and statistical performance boundaries of five matched-field processing (MFP) methods—Bartlett, Minimum Variance Distortionless Response (MVDR), Multiple Signal Classification (MUSIC), Reduced Covariance Matrix [...] Read more.

Passive acoustic localization in complex shallow waters requires algorithms tailored to specific operational constraints. This paper investigates the adaptability, computational efficiency, and statistical performance boundaries of five matched-field processing (MFP) methods—Bartlett, Minimum Variance Distortionless Response (MVDR), Multiple Signal Classification (MUSIC), Reduced Covariance Matrix (RCM), and Rank and Trace Minimization (RTM)—using the Elba-93 sea trial dataset. Error metrics and processing complexities are systematically evaluated across stationary and maneuvering target scenarios. Rigorous non-parametric statistical tests reveal distinct operational boundaries: under stationary conditions dominated by systemic environmental mismatch, energy-based processors guarantee reliable baseline stability. Conversely, under snapshot-deficient dynamic conditions tracking a receding target, standard high-resolution subspace methods become highly vulnerable to trajectory jumps. In such highly dynamic scenarios, adaptive energy-based processors (specifically MVDR) exhibit the most stable tracking continuity and lowest numerical peak errors. Simultaneously, the operational adaptability of subspace methods is improved via covariance matrix reconstruction (CMR). Specifically, the RCM technique effectively decouples unstructured sensor noise, mitigating maximum trajectory deviations and providing a balanced trade-off between computational efficiency and robustness. Statistical evaluations confirm the fundamental performance boundaries in static environments, while highlighting sample-size limitations in highly dynamic scenarios, thereby establishing a realistic, evidence-based benchmark for marine engineering applications. 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, 1233 KB

Open AccessArticle

AoI Minimization Scheduling Using Integrated Collection-Relay in Multi-AUV Multi-Hop Underwater Networks

by Sanghwa Lee, Minho Kim, Seunghwan Seol and Jaehak Chung

Electronics 2026, 15(13), 2930; https://doi.org/10.3390/electronics15132930 - 3 Jul 2026

Viewed by 172

Abstract

Underwater surveillance systems require a multi-AUV-based multi-hop underwater data collection system to deliver data from sensor nodes distributed over a wide underwater area to a buoy. In this system, the node-visit of the data collection AUV and the relay transmission to the relay [...] Read more.

Underwater surveillance systems require a multi-AUV-based multi-hop underwater data collection system to deliver data from sensor nodes distributed over a wide underwater area to a buoy. In this system, the node-visit of the data collection AUV and the relay transmission to the relay AUV jointly affect the end-to-end Age of Information (AoI) from each node to the buoy. This paper proposes a discrete soft actor–critic (SAC)-based integrated scheduling policy that jointly determines the node-visit and relay transmission of the data collection AUV within a single decision-making process. The proposed method represents the end-to-end information update process from data collection to buoy update in the state design and derives a relationship showing that the accumulated node-wise max AoI increment corresponds to the Mean Peak AoI and uses this increment as the decision step reward. Computer simulation results show that the proposed method achieves a lower Mean Peak AoI and higher Delivery than conventional methods that determine node visits and relay transmissions separately, including Visit-Only SAC, Age-Greedy threshold, and Age-Gain TX. The cumulative distribution function (CDF) analysis also shows more stable performance across episodes. Full article

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

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28 pages, 1285 KB

Open AccessArticle

Embedded Mixture-Correntropy Spatial Smoothing for Robust DOA Estimation in Shallow-Water Underwater Acoustics

by Guanquan Dai, Yang Shi and Fei-Yun Wu

J. Mar. Sci. Eng. 2026, 14(10), 957; https://doi.org/10.3390/jmse14100957 - 21 May 2026

Viewed by 245

Abstract

Direction-of-arrival (DOA) estimation in shallow-water underwater acoustics is challenged by coherent multipath and impulsive disturbances, which jointly cause covariance rank deficiency and outlier-driven subspace distortion. This paper proposes an embedded robust covariance-construction mechanism for coherent-plus-impulsive DOA estimation. The mechanism is implemented as mixture-correntropy-weighted [...] Read more.

Direction-of-arrival (DOA) estimation in shallow-water underwater acoustics is challenged by coherent multipath and impulsive disturbances, which jointly cause covariance rank deficiency and outlier-driven subspace distortion. This paper proposes an embedded robust covariance-construction mechanism for coherent-plus-impulsive DOA estimation. The mechanism is implemented as mixture-correntropy-weighted simplified spatial smoothing (SS–MCC), in which snapshot reliability is enforced during subarray covariance accumulation rather than after decorrelation. A two-kernel residual-based weighting rule suppresses strongly contaminated snapshots while retaining moderately perturbed but informative snapshots. Under a controlled narrowband uniform linear array benchmark with fully coherent two-arrival multipath and Bernoulli–Gaussian impulsive noise, SS–MCC yields more stable DOA behavior than MUSIC, SS-MUSIC, and FLOM-MUSIC, especially in low-SNR, high-impulsiveness, and near-threshold regimes, although absolute strict recovery remains limited in the hardest cases. All-trial strict correct-two-peak statistics and ablation results show that the gain mainly comes from embedded covariance cleaning rather than post-processing or parameter tuning. A measured-noise-injected benchmark using NOAA–Navy SanctSound FK01 underwater recordings further confirms the same qualitative robustness trend under real noise waveforms, while remaining a semi-realistic noise-injection check rather than measured-array sea-trial validation. A simplified DOA- assisted MVDR benchmark indicates that improved covariance robustness can also support more favorable beamforming-oriented trends. The results provide controlled benchmark evidence that reliability-aware covariance construction can stabilize subspace extraction under joint coherent multipath and impulsive contamination; validation under wideband propagation, model mismatch, partial coherence, and measured array data remains future work. Full article

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

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15 pages, 2919 KB

Open AccessArticle

Multiscale Attention-Based Whistle Segmentation for Biomimetic Communication

by Minho Kim, Seunghwan Seol, Yongcheol Kim, Geun-Ho Park and Jaehak Chung

Electronics 2026, 15(10), 2147; https://doi.org/10.3390/electronics15102147 - 16 May 2026

Viewed by 331

Abstract

This paper proposes a deep learning-based segmentation model for biomimetic underwater acoustic communication systems, capable of extracting dolphin whistle signals from underwater acoustic data containing diverse noise sources while reducing contour discontinuity. The proposed model is based on a U-Net architecture and incorporates [...] Read more.

This paper proposes a deep learning-based segmentation model for biomimetic underwater acoustic communication systems, capable of extracting dolphin whistle signals from underwater acoustic data containing diverse noise sources while reducing contour discontinuity. The proposed model is based on a U-Net architecture and incorporates the Convolutional Block Attention Module (CBAM) and the Multi-Scale Combining Spatial Attention Module (MSC-SAM) to capture whistle features at multiple resolutions. CBAM is applied to skip connections to emphasize meaningful channels and spatial information related to whistle contours. In addition, MSC-SAM is designed to integrate spatial attention maps generated at each resolution, thereby reducing whistle contour discontinuity that occurs during the segmentation process. The proposed model is evaluated using acoustic data collected by the National Oceanic and Atmospheric Administration (NOAA) and compared with existing models, including U-Net, ResU-Net, U-Net++, FPN, PSPNet, DeepLabv3+, and MA-Net. Experimental results show that the proposed model achieves improved segmentation performance in terms of pixel accuracy, IoU, and Dice score. For the proposed whistle-level evaluation criteria, namely Discontinuous Whistles and Missed Whistles, the proposed model reduces discontinuous whistles and missed detections by 65.6% and 61.1%, respectively, relative to U-Net. 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, 4919 KB

Open AccessArticle

Multiplepath Matching Pursuit Using a Random Virtual Array Set Construction and Validation Technology for Target Bearing Detection with an Underwater Vector Coprime Array

by Xiao Chen, Ying Zhang, Yuan An and Zhen Wang

J. Mar. Sci. Eng. 2026, 14(6), 583; https://doi.org/10.3390/jmse14060583 - 21 Mar 2026

Viewed by 468

Abstract

The coprime array, proposed in recent years as a special type of sparse array, combines the advantages of sparse sensing with the unique properties of prime numbers, enabling a larger array aperture and higher degrees of freedom with the same number of physical [...] Read more.

The coprime array, proposed in recent years as a special type of sparse array, combines the advantages of sparse sensing with the unique properties of prime numbers, enabling a larger array aperture and higher degrees of freedom with the same number of physical sensors. In underwater array signal processing, the high-resolution potential of coprime arrays has attracted significant attention. However, in complex ocean environments, leveraging the advantages of coprime arrays to achieve high-resolution and robust target detection still faces challenges posed by sensor failures. Element failures can disrupt the physical structure of the coprime array, leading to significantly increased energy in grating lobes and side lobes of the beam pattern, thereby raising the probability of false target azimuth identification. To address this issue, this paper analyzes the virtual array set mapped from the physical coprime array and proposes a multiplepath matching pursuit method for underwater vector coprime array target azimuth detection based on random virtual array set construction and verification techniques. Cases of continuous and non-continuous virtual arrays are analyzed, and corresponding solutions are proposed. Through simulations and analyses of sea trial data, it is demonstrated that the proposed method can achieve high-resolution target azimuth detection as well as robust target detection in the presence of physical sensor failures. Full article

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

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17 pages, 4533 KB

Open AccessArticle

Depth Conversion of Underwater Static Electric Fields for Submersibles in High-Latitude Low-Temperature Sea Areas

by Yuhong Li, Cong Chen, Hongsen Zhao, Yiqun Liu, Yunfu Hou, Jiaqing Sun and Wentie Yang

J. Mar. Sci. Eng. 2026, 14(6), 536; https://doi.org/10.3390/jmse14060536 - 12 Mar 2026

Viewed by 360

Abstract

Depth conversion of underwater static electric fields refers to a mathematical approach that indirectly determines the distribution of planar electric fields at larger depths using measured planar electric field data obtained from a shallower region with finite depth and limited area. The complicated [...] Read more.

Depth conversion of underwater static electric fields refers to a mathematical approach that indirectly determines the distribution of planar electric fields at larger depths using measured planar electric field data obtained from a shallower region with finite depth and limited area. The complicated environment of high-latitude low-temperature sea areas further increases the difficulty of performing practical large-depth measurements of underwater electric fields. Therefore, depth conversion becomes an important technical strategy for overcoming the constraints of field measurements and for comprehensively understanding the distribution of underwater static electric fields of the target. This study begins with the mathematical formulation of the depth conversion problem, solves the related boundary value problem, and develops the corresponding depth conversion method. Subsequently, based on COMSOL simulation data of the underwater static electric field generated by a scaled-down submersible model, numerical analyses are conducted to investigate the effects of factors such as grid discretization, measurement plane dimensions, conversion depth, and data noise on the conversion accuracy. Finally, the reliability of the conversion method is validated in a laboratory environment by simulating a naturally frozen sea area and employing measured underwater static electric field data from the scaled-down submersible model. The results demonstrate that the developed conversion method can effectively achieve extrapolation of the underwater static electric field of the submersible from shallow regions to deeper water. Even when the noise amplitude is nearly twice that of the effective signal and the conversion depth reaches 8 times the outer diameter of the submersible, the relative root mean square error (RRMSE) of the conversion results can still be maintained below 0.10. These findings provide useful references for the advancement of technologies related to underwater electric field characteristic recognition and electric field stealth performance evaluation in high-latitude low-temperature sea areas. 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, 2616 KB

Open AccessArticle

Long-Range Source Localization in the Deep Sea Using Adaptive FDSL with a Few-Element Array

by Jingwen Yin, Haklim Ko and Hojun Lee

Sensors 2026, 26(5), 1495; https://doi.org/10.3390/s26051495 - 27 Feb 2026

Viewed by 532

Abstract

Matched Field Processing (MFP) suffers from environmental mismatch in deep-sea long-range source localization. Although Frequency Difference Matched Field Processing (FDMFP) improves mismatch tolerance, it fails due to caustic phase effects. Frequency Difference Source Localization (FDSL) effectively compensates for caustic phase errors by applying [...] Read more.

Matched Field Processing (MFP) suffers from environmental mismatch in deep-sea long-range source localization. Although Frequency Difference Matched Field Processing (FDMFP) improves mismatch tolerance, it fails due to caustic phase effects. Frequency Difference Source Localization (FDSL) effectively compensates for caustic phase errors by applying frequency-difference processing to both the measured field and the replica field. However, conventional FDSL typically relies on large-aperture arrays with numerous elements, resulting in high deployment costs and bulky systems. Furthermore, it exhibits limited resolution and elevated sidelobes. These limitations are exacerbated under reduced element counts and low signal-to-noise ratio (SNR) conditions. To improve performance under low SNR and small-array configurations, this paper proposes the FDSL-MVDR and FDSL-MUSIC methods by deriving adaptive weight vectors based on the frequency-difference covariance structure and redefining the ambiguity surface. Numerical simulations in a deep-sea Munk environment (source range 195 km, depth 1000 m) using a 15-element vertical line array demonstrate that the adaptive FDSL methods outperform conventional FDSL in terms of peak sharpness and sidelobe suppression. FDSL-MUSIC achieves approximately 100% localization success at SNR = −5 dB, a 4 dB improvement over conventional FDSL. Performance analyses under representative environmental mismatches indicate that the adaptive FDSL methods maintain robust localization performance and high-resolution characteristics in complex deep-sea environments. These results validate the feasibility of high-precision deep-sea localization using a few-element array. Full article

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

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30 pages, 14583 KB

Open AccessArticle

MF-CLF: Multi-Feature Chrominance–Luminance Fusion for Blind Underwater Image Quality Assessment

by Wei Chen, Yi Zhang, Damon M. Chandler and Mikolaj Leszczuk

J. Mar. Sci. Eng. 2026, 14(4), 333; https://doi.org/10.3390/jmse14040333 - 9 Feb 2026

Cited by 1 | Viewed by 670

Abstract

Underwater images commonly exhibit blurring, color casts, and low contrast due to light attenuation and scattering in water. Although numerous underwater image enhancement (UIE) algorithms have been developed to improve the usability of underwater imaging systems, evaluating the performance of these algorithms remains [...] Read more.

Underwater images commonly exhibit blurring, color casts, and low contrast due to light attenuation and scattering in water. Although numerous underwater image enhancement (UIE) algorithms have been developed to improve the usability of underwater imaging systems, evaluating the performance of these algorithms remains challenging due to the lack of reference images. Thus, blind/no-reference (NR) underwater image quality assessment (UIQA) has emerged as a key research focus. While existing NR-UIQA methods based on luminance and chrominance cues have shown effectiveness, modeling these attributes separately ignores valuable information arising from their joint behavior, since underwater degradations often induce simultaneous changes in luminance and chrominance that cannot be reliably characterized by either attribute alone. In this paper, we propose a lightweight and explainable NR-UIQA method, called multi-feature chrominance–luminance fusion (MF-CLF), based on jointly modeling the intra- and cross-attribute dependencies among chrominance and luminance statistics. Specifically, our approach constructs chrominance-attribute features across multiple color spaces, extracts luminance-attribute features using multi-kernel perceptual descriptors, and models the chrominance–luminance characteristics by explicitly capturing the interactions between the luminance and chrominance attributes. The extracted features are then mapped into a quality score using a support vector machine (SVM), enabling objective and reliable underwater image quality prediction. Experimental results tested on four public benchmark datasets demonstrate that MF-CLF significantly outperforms among lightweight, statistical-learning-based methods. Specifically, our approach achieves an SROCC value of 0.864 on the SAUD2.0 dataset, outperforming existing methods by 20.3%, and demonstrates strong robustness in cross-dataset evaluations with an SROCC value of 0.737, which is more than twice that of the traditional methods. Full article

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

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38 pages, 3431 KB

Open AccessArticle

Transmitting Images in Difficult Environments Using Acoustics, SDR and GNU Radio Applications

by Michael Alldritt and Robin Braun

Electronics 2026, 15(3), 678; https://doi.org/10.3390/electronics15030678 - 4 Feb 2026

Cited by 1 | Viewed by 960

Abstract

This paper explores the feasibility of using acoustic wave propagation, particularly in the ultrasonic range, as a solution for data transmission in environments where traditional radio frequency (RF) communication is ineffective due to signal attenuation—such as in liquids or dense media like metal [...] Read more.

This paper explores the feasibility of using acoustic wave propagation, particularly in the ultrasonic range, as a solution for data transmission in environments where traditional radio frequency (RF) communication is ineffective due to signal attenuation—such as in liquids or dense media like metal or stone. Leveraging GNU Radio and commercially available audio hardware, a low-cost, SDR (Software Defined Radio) system was developed to transmit data blocks (e.g., images, text, and audio) through various substances. The system employs BFSK (Binary Frequency Shift Keying) and BPSK (Binary Phase Shift Keying), operates at ultrasonic frequencies (typically 40 kHz), and has performance validated under real-world conditions, including water, viscous substances, and flammable liquids such as hydrocarbon fuels. Experimental results demonstrate reliable, continuous communication at Nyquist–Shannon sampling rates, with effective demodulation and file reconstruction. The methodology builds on concepts originally developed for Ad Hoc Sensor Networks in shipping containers, extending their applicability to submerged and RF-hostile environments. The modularity and flexibility of the GNU Radio platform allow for rapid adaptation across different media and deployment contexts. This work provides a reproducible and scalable communication solution for scenarios where RF transmission is impractical, offering potential applications in underwater sensing, industrial monitoring, railways, and enclosed infrastructure diagnostics. Across controlled laboratory experiments, the system achieved 100% successful reconstruction of transmitted image files up to 100 kB and sustained packet delivery success exceeding 98% under stable coupling conditions. Full article

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

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20 pages, 1803 KB

Open AccessArticle

Adaptive Localization-Free Secure Routing Protocol for Underwater Sensor Networks

by Ayman Alharbi and Saleh Ibrahim

Sensors 2026, 26(1), 17; https://doi.org/10.3390/s26010017 - 19 Dec 2025

Cited by 2 | Viewed by 642

Abstract

Depth-based probabilistic routing (DPR) is an efficient underwater acoustic network (UAN) routing protocol which resists the depth-spoofing attack. DPR’s optimal value of the unqualified forwarding probability depends on the UAN topology, condition, and threat state, which are highly dynamic. If the static forwarding [...] Read more.

Depth-based probabilistic routing (DPR) is an efficient underwater acoustic network (UAN) routing protocol which resists the depth-spoofing attack. DPR’s optimal value of the unqualified forwarding probability depends on the UAN topology, condition, and threat state, which are highly dynamic. If the static forwarding probability used in DPR is set too low for the current state, packet delivery ratio (PDR) drops. If it is set too high, unnecessary forwarding occurs when the network is not under attack, thus wasting valuable energy. In this paper, we propose a novel routing protocol, which uses a feedback mechanism that allows the sink to continuously adapt the unqualified forwarding probability according to the current network state. The protocol aims to achieve an application-controlled desired delivery ratio using one of three proposed update algorithms developed in this work. We analyze the performance of the proposed algorithms through simulation. Results demonstrate that the proposed adaptive routing protocol achieves resilience to depth-spoofing attacks by successfully delivering more than 80% of generated packets in more than 95% of simulated networks, while avoiding unnecessary unqualified forwarding in normal conditions. Full article

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

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20 pages, 6749 KB

Open AccessArticle

An Improved RANSAC Method for Outlier Detection in OBN Acoustic Positioning

by Yijun Yang, Cuilin Kuang, Baocai Yang, Haonan Zhang, Tao Cui and Kaiwei Sang

Appl. Sci. 2025, 15(23), 12732; https://doi.org/10.3390/app152312732 - 1 Dec 2025

Viewed by 678

Abstract

In ocean bottom node (OBN) seismic exploration, the precise positioning of OBNs directly affects seismic data quality. However, complex marine environments often introduce intricate outliers into collected acoustic positioning data, which severely restricts the positioning accuracy and stability of OBNs. To address issues [...] Read more.

In ocean bottom node (OBN) seismic exploration, the precise positioning of OBNs directly affects seismic data quality. However, complex marine environments often introduce intricate outliers into collected acoustic positioning data, which severely restricts the positioning accuracy and stability of OBNs. To address issues such as poor threshold adaptability and low continuous outlier detection capability in existing outlier detection methods when processing OBN acoustic observation data, this paper proposes a quality control method for seabed acoustic positioning based on an improved Random Sample Consensus (RANSAC) method. This method employs a dynamic threshold that adapts to the observation fitting value and inlier rate, and introduces time-series uniform grouping sampling, thereby optimizing threshold setting and sampling strategy to enhance outlier detection performance and computational efficiency. Simulation results demonstrate that compared to the conventional RANSAC, the improved method exhibits superior outlier detection performance and computational efficiency, while achieving optimal positioning accuracy. Field experiment results demonstrate that the improved method can effectively detect and eliminate both large and small outliers, as well as continuous outliers. Compared to the fixed-threshold method, the improved RANSAC method improves positioning accuracy by 28.8% and 42.2% in the Direction Alongline (DA) and Direction Crossline (DC), respectively. Additionally, it achieves a 13.3% improvement in DA positioning accuracy and a 49.0% increase in computational efficiency over the conventional RANSAC method. The research findings demonstrate that the improved RANSAC method effectively enhances the accuracy and efficiency of OBN positioning, providing technical support for high-precision positioning in complex marine seismic exploration. 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, 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

Cited by 3 | Viewed by 1157

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

Cited by 1 | Viewed by 880

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

Cited by 1 | Viewed by 1423

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

Cited by 1 | Viewed by 1311

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

Cited by 1 | Viewed by 1032

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 875

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

Cited by 2 | Viewed by 983

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

Cited by 1 | Viewed by 1349

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 1737

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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