IEEE Journal of Oceanic Engineering最新文献

{"title":"Coherence-Based Inversion of Pulse-Echo Data for Ocean Sound Speed Estimation","authors":"Mohammad Reza Mousavi;Len Zedel","doi":"10.1109/JOE.2026.3688071","DOIUrl":"https://doi.org/10.1109/JOE.2026.3688071","url":null,"abstract":"Ocean sound speed profile measurement is critical for marine applications, yet current methods necessitate direct sensor deployment. This article introduces a method for remote ocean sound speed measurement inspired by techniques used in medical ultrasound. A single acoustic transmitter projects sound, and a linear array of receivers is used to record sound reflected by naturally occurring targets. An inversion approach is used to optimize coherence among the received signals by searching for trial average sound speed and scatterer locations. For each scatterer location, the average sound speed maximizing the signal alignment is considered as the estimation. The method's effectiveness is validated using a ray acoustic propagation model. We analyze system performance subject to system parameters, including array length, number of receivers, and pulse characteristics, alongside environmental conditions. Compared to medical ultrasound, the ocean environment has several key advantages, including horizontal stratification, the presence of distinct reflectors, and the feasibility of repeated transmissions with correlation-based averaging, which together make the proposed method efficient. With the proposed method, average and local sound speed estimations yield root-mean-square errors of less than 1 and 5 m/s, respectively, for depths between 10 and 50 m. These results are achievable using an 8-m array with 20 receivers and 20 pulse repetitions with the center frequency of 300 kHz.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2390-2408"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Training-Free Underwater 3-D Object Detection From Sonar Point Clouds: A Comparison of Traditional and Deep Learning Approaches","authors":"Muhammad Salman Shaukat;Yannik Käckenmeister;Sebastian Bader;Thomas Kirste","doi":"10.1109/JOE.2026.3675841","DOIUrl":"https://doi.org/10.1109/JOE.2026.3675841","url":null,"abstract":"Underwater 3-D object detection remains one of the most challenging frontiers in computer vision, where traditional approaches struggle with the harsh acoustic environment and scarcity of training data. While deep learning has revolutionized terrestrial 3-D detection, its application underwater faces a critical bottleneck: obtaining sufficient annotated sonar data is prohibitively expensive and logistically complex, often requiring specialized vessels, expert surveyors, and favorable weather conditions. This work addresses a fundamental question: Can we achieve reliable underwater 3-D object detection without real-world training data? We tackle this challenge by developing and comparing two paradigms for training-free detection of artificial structures in multibeam echo-sounder point clouds. Our dual approach combines a physics-based sonar simulation pipeline that generates synthetic training data for state-of-the-art neural networks, with a robust model-based template matching system that leverages geometric priors of target objects. Evaluation on real bathymetry surveys from the Baltic Sea reveals important insights: neural networks trained on synthetic data suffer severe domain shift, with performance dropping by approximately 70% from simulated to real sonar data (30% mAP at IoU = 0.5). Our template matching approach proves far more stable, experiencing an approximate 30% drop from simulation to reality and maintaining 68% mAP on real data without requiring any training, demonstrating remarkable robustness to acoustic noise and environmental variations. Our findings challenge conventional wisdom about data-hungry deep learning in underwater domains and establish an initial comparative benchmark for training-free underwater 3D detection on large-area seafloor surveys. This work opens new possibilities for autonomous underwater vehicle navigation, marine archaeology, and offshore infrastructure monitoring in data-scarce environments where traditional machine learning approaches fail.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2284-2297"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Anomaly Detection for Hydraulic System in Deep-Sea Submersible","authors":"Xing Fang;Xin Tan;Chengxi Zhang;Xiang Gao;Choon Ki Ahn","doi":"10.1109/JOE.2026.3674398","DOIUrl":"https://doi.org/10.1109/JOE.2026.3674398","url":null,"abstract":"The hydraulic system used in the deep-sea submersible of China works in the extreme conditions of the deep-sea environment, where the pressure is high, the temperature is low, and the landscape is complicated, which contributes to the risks of critical failures. Anomaly detection is essential to the safety of crews and mission success. However, traditional techniques have two problems: small samples of faults and severe class imbalance (with few faults hidden in the vast sea of normal activity). To cope with these issues, we suggest the Aux-BiGAN, an unsupervised anomaly detector that is specific to the hydraulic system. The approach enhances learning normal data distributions and increases the sensitivity of anomaly detection but traines on normal data only by auxiliary losses. It is tested on actual hydraulic system defects and artificial data of hard failures and incipient soft anomalies. In real-world conditions, it is able to identify severe failures, including hydraulic oil leakage, electronic control unit burnout, and sticking solenoid valves, and its results are supported by postdive maintenance reports by pilots. Experimental results demonstrate the effectiveness of the proposed method, which achieves an area under the receiver operating characteristic curve of 0.9104 on hard anomalies. It outperforms the baseline BiGAN across all metrics. It surpasses state-of-the-art methods in terms of accuracy, F1-score, and false alarm rate across both failure types, validating its enhanced reliability for deep-sea missions.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"1977-1992"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Study of Underwater Acoustical Reconfigurable Intelligent Surfaces With Synthetic Reflection","authors":"Yu Luo;Lina Pu;Aijun Song","doi":"10.1109/JOE.2025.3617913","DOIUrl":"https://doi.org/10.1109/JOE.2025.3617913","url":null,"abstract":"This article presents an underwater acoustical reconfigurable intelligent surface (UA-RIS) designed for long-range, high-speed, and environmentally friendly communication in oceanic environments. The proposed UA-RIS comprises multiple pairs of acoustical reflectors that utilize a synthetic reflection scheme to flexibly control the amplitude and phase of reflected waves. This capability enables precise beam steering to enhance or attenuate sound levels in specific directions. A prototype UA-RIS with 4 × 6 acoustical reflection units is constructed and tested in both tank and lake environments to evaluate performance. Experimental results using a continuous wave as the source signal demonstrate that the prototype is capable of effectively pointing reflected waves to targeted directions while minimizing sidelobes through synthetic reflection. Field tests reveal that deploying the UA-RIS on the sender side considerably extends communication ranges by 28% in deep water and 46% in shallow waters. Furthermore, with a fixed communication distance, positioning the UA-RIS at the transmitter side substantially boosts the receiving signal-to-noise ratio (SNR), with an average increase of 2.13 dB and peaks up to 2.92 dB. When positioned on the receiver side, the UA-RIS can expand the communication range in shallow and deep water environments by 40.6% and 66%, respectively. Moreover, placing the UA-RIS close to the receiver enhances SNR by an average of 2.56 dB, reaching up to 4.2 dB under certain circumstances.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2358-2373"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WaterDiff: Mamba-Based Latent Diffusion Model for Underwater Image Enhancement","authors":"Meisheng Guan;Haiyong Xu;Gangyi Jiang;Mei Yu;Yeyao Chen;Ting Luo;Xuebo Zhang","doi":"10.1109/JOE.2026.3674692","DOIUrl":"https://doi.org/10.1109/JOE.2026.3674692","url":null,"abstract":"Underwater imaging is affected by absorption and scattering, leading to issues, such as low contrast, poor clarity, color deviation, and haze. To address these problems, we propose a Mamba-based latent diffusion model (LDM) for underwater image enhancement, named WaterDiff, which leverages the potential of LDM and the guidance provided by the transmission map. To alleviate the substantial computational cost incurred by pixel-based diffusion recovery, a latent-based method is utilized, whereby the degraded underwater image is first transformed by a feature encoder and then restored in the compact latent space. To address these challenges, two key components are proposed: first, a feature encoder that extracts the multiscale features enhanced in the decoder to reduce distortions; second, Mamba encoder with linear complexity and transmission map guidance module integrated into the noise predictor to extract structural information, which enables more powerful latent-based image enhancement without significantly increasing inference costs. The experimental results demonstrate the superiority of the proposed WaterDiff and effectiveness of its modules.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2444-2464"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring Ship Movements With Seafloor Optical-Fiber Distributed Acoustic Sensing","authors":"Jianmin Lin;Chen Ji;Xingliang Sun;Wen Xu","doi":"10.1109/JOE.2026.3664678","DOIUrl":"https://doi.org/10.1109/JOE.2026.3664678","url":null,"abstract":"Long-term passive acoustic ship surveillance is expensive and seldom undertaken. Distributed acoustic sensing (DAS) is an emerging technology whereby optical-fiber (OF) cables are used as dense acoustic sensor arrays, offering an alternative for marine monitoring. In this article, we describe DAS-based ship surveillance using a predeployed OF cable in shallow water, where ship-radiated noise was successfully detected when a tanker passed overhead. Typical spectral signatures of ship-radiated noise were observed, and the effective durations of detection were found to be influenced by DAS inherent angular sensitivity. The use of two adjacent cable segments of different orientation allowed precise estimation of ship azimuth, and ship velocity was estimated by DAS frequency–wave number spectrum analysis. Both estimated ship azimuth and velocity were consistent with Automatic Identification System data. Our results indicate the potential of seafloor OF-cable-based DAS in ship monitoring.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"1651-1661"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PLUM: A Prompt-Guided Lightweight Unified Model for Enhancement of Multidegraded Underwater Image","authors":"Alik Pramanick;Akshay Daydar;Sonal Kumar;Arijit Sur;V. Vijaya Saradhi","doi":"10.1109/JOE.2026.3680311","DOIUrl":"https://doi.org/10.1109/JOE.2026.3680311","url":null,"abstract":"Degradation in underwater images is commonly caused by haze, low contrast, blur, and color distortion, which can hinder the effectiveness of many computer vision tasks. Most of the existing image enhancement works treat these degradations separately, thus limiting their applicability. To handle this problem, we propose a unified model to learn multiple degradations concurrently, improving resilience to practical conditions. First, we generate various degraded versions (haze, blur, low-contrast, and color distortion) of the input degraded image through domain translation. Next, original degraded and translated images are fed into a novel multidegraded enhancement network, which employs multiple encoders with an intermediate feature-swapping mechanism and a single decoder. Each encoder block enhances the network’s ability to characterize internal relationships in spatial and frequency domains by incorporating the fast Fourier transform. Intermediate feature swapping encourages the model to learn more robust representations. The decoder block contains the proposed prompt-guided spatial-frequency cross-attention block to encode degraded visual information effectively. Extensive experiments on several underwater image enhancement data sets demonstrate that our model outperforms the state-of-the-art methods with less computational cost (e.g., 1.20% structural similarity index boost on UIEB data set with 84.1% less parameter).","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2516-2531"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seabed Wide-Angle Reflection Coefficient and Source Radiation Pattern Measurements From a Low-Frequency Multibeam Sonar","authors":"Charles W. Holland;Cody R. Henderson;Tim Sonnemann;Bjørnar Langli;Daniel L. Orange","doi":"10.1109/JOE.2026.3691004","DOIUrl":"https://doi.org/10.1109/JOE.2026.3691004","url":null,"abstract":"In acoustic remote sensing, the seabed frequency-domain reflection coefficient contains significant geoacoustic information, namely the depth and frequency dependence of sediment sound speed, density, and attenuation. Typical reflection measurements are performed with a towed source and/or receivers to obtain a range of seabed reflected angles. It is shown here that the measurements can be performed using a hull-mounted low-frequency multibeam sonar with an offboard fixed or drifting hydrophone. To obtain the reflection coefficient (as well as scattering strength and other quantities of potential interest), the multibeam sonar radiation pattern must be known. Radiation patterns and absolute levels of the Kongsberg SBP29 multibeam sonar are measured and reflection coefficient measurements are presented in two geologically dissimilar environments, the Eel River shelf, CA and the Santa Barbara Basin, CA. The reflection coefficient frequency and angle dependences are explored via forward modeling to determine regions of the data space that contain information about the geoacoustic properties.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2298-2313"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11570775","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring Ship Movements With Seafloor Optical-Fiber Distributed Acoustic Sensing","authors":"Jianmin Lin;Chen Ji;Xingliang Sun;Wen Xu","doi":"10.1109/JOE.2026.3664678","DOIUrl":"https://doi.org/10.1109/JOE.2026.3664678","url":null,"abstract":"Long-term passive acoustic ship surveillance is expensive and seldom undertaken. Distributed acoustic sensing (DAS) is an emerging technology whereby optical-fiber (OF) cables are used as dense acoustic sensor arrays, offering an alternative for marine monitoring. In this article, we describe DAS-based ship surveillance using a predeployed OF cable in shallow water, where ship-radiated noise was successfully detected when a tanker passed overhead. Typical spectral signatures of ship-radiated noise were observed, and the effective durations of detection were found to be influenced by DAS inherent angular sensitivity. The use of two adjacent cable segments of different orientation allowed precise estimation of ship azimuth, and ship velocity was estimated by DAS frequency–wave number spectrum analysis. Both estimated ship azimuth and velocity were consistent with Automatic Identification System data. Our results indicate the potential of seafloor OF-cable-based DAS in ship monitoring.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"1651-1661"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WaterDiff: Mamba-Based Latent Diffusion Model for Underwater Image Enhancement","authors":"Meisheng Guan;Haiyong Xu;Gangyi Jiang;Mei Yu;Yeyao Chen;Ting Luo;Xuebo Zhang","doi":"10.1109/JOE.2026.3674692","DOIUrl":"https://doi.org/10.1109/JOE.2026.3674692","url":null,"abstract":"Underwater imaging is affected by absorption and scattering, leading to issues, such as low contrast, poor clarity, color deviation, and haze. To address these problems, we propose a Mamba-based latent diffusion model (LDM) for underwater image enhancement, named WaterDiff, which leverages the potential of LDM and the guidance provided by the transmission map. To alleviate the substantial computational cost incurred by pixel-based diffusion recovery, a latent-based method is utilized, whereby the degraded underwater image is first transformed by a feature encoder and then restored in the compact latent space. To address these challenges, two key components are proposed: first, a feature encoder that extracts the multiscale features enhanced in the decoder to reduce distortions; second, Mamba encoder with linear complexity and transmission map guidance module integrated into the noise predictor to extract structural information, which enables more powerful latent-based image enhancement without significantly increasing inference costs. The experimental results demonstrate the superiority of the proposed WaterDiff and effectiveness of its modules.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2444-2464"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}