Search Results (169)

This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports real-time and distributed processing of hydrophone data collected in diverse aquatic environments. Acoustic signals captured by heterogeneous hydrophones—featuring varying sensitivity and bandwidth—are streamed to the cloud, where several machine learning algorithms can be deployed to extract distinguishing acoustic signatures from vessel engines and propellers in interaction with water. The architecture leverages cloud-based services for data ingestion, processing, and storage, facilitating robust vehicle detection and localization through propagation modeling and multi-array geometric configurations. Experimental validation demonstrates the system’s effectiveness in handling high-volume acoustic data streams while maintaining low-latency processing. The proposed approach highlights the potential of cloud technologies to deliver scalable, resilient, and adaptive acoustic sensing platforms for applications in maritime traffic monitoring, harbor security, and environmental surveillance. Full article

Accurate localization plays a critical role in enabling underwater vehicle autonomy. In this work, we develop a robust infrastructure-based localization framework that estimates the position and orientation of underwater vehicles using only range measurements from long baseline (LBL) acoustic beacons to multiple on-board receivers. The proposed framework integrates three key components, each formulated as a convex optimization problem. First, we introduce a robust calibration function that unifies multiple sources of measurement error—such as range-dependent degradation, variable sound speed, and latency—by modeling them through a monotonic function. This function bounds the true distance and defines a convex feasible set for each receiver location. Next, we estimate the receiver positions as the center of this feasible region, using two notions of centrality: the Chebyshev center and the maximum volume inscribed ellipsoid (MVE), both formulated as convex programs. Finally, we recover the vehicle’s full 6-DOF pose by enforcing rigid-body constraints on the estimated receiver positions. To do this, we leverage the known geometric configuration of the receivers in the vehicle and solve the Orthogonal Procrustes Problem to compute the rotation matrix that best aligns the estimated and known configurations, thereby correcting the position estimates and determining the vehicle orientation. We evaluate the proposed method through both numerical simulations and field experiments. To further enhance robustness under real-world conditions, we model beacon-location uncertainty—due to mooring slack and water currents—as bounded spherical regions around nominal beacon positions. We then mitigate the uncertainty by integrating the modified range constraints into the MVE position estimation formulation, ensuring reliable localization even under infrastructure drift. Full article

The rapid expansion of deep-sea sensing networks underscores the critical need for accurate underwater positioning of observation base stations. However, achieving precise acoustic localization, particularly at depths exceeding 4 km, remains a significant challenge due to systematic ranging errors, clock drift, and inaccuracies in sound speed modeling. This study proposes and validates a three-tier calibration framework consisting of a Dynamic Single-Difference (DSD) solver, a geometrically optimized reference buoy selection algorithm, and a Virtual Beacon (VB) depth inversion method based on sound speed profiles. Through simulations under varying noise conditions, the DSD method effectively mitigates common ranging and clock errors. The geometric reference optimization algorithm enhances the selection of optimal buoy layouts and reference points. At a depth of 4 km, the VB method improves vertical positioning accuracy by 15% compared to the DSD method alone, and nearly doubles vertical accuracy compared to traditional non-differential approaches. This research demonstrates that deep-sea underwater target calibration can be achieved without high-precision time synchronization and in the presence of fixed ranging errors. The proposed framework has the potential to lower technological barriers for large-scale deep-sea network deployments and provides a robust foundation for autonomous deep-sea exploration. Full article

This study introduces an algorithm for quantifying oceanic wind speed and identifying sound sources in the local underwater soundscape. Utilizing low-complexity metrics like one-minute spectral kurtosis and power spectral density levels, the algorithm categorizes different soundscapes and estimates wind speed. It detects rain, vessels, fin and blue whales, as well as clicks and whistles from dolphins. Positioned as a foundational tool for implementing the Ocean Sound Essential Ocean Variable (EOV), it contributes to understanding long-term trends in climate change for sustainable ocean health and predicting threats through forecasts. The proposed soundscape classification algorithm, validated using extensive acoustic recordings (≥32 kHz) collected at various depths and latitudes, demonstrates high performance, achieving an average precision of $89\%$ and an average recall of $86.59\%$ through optimized parameter tuning via a genetic algorithm. Here, wind speed is determined using a cubic function with power spectral density (PSD) at 6 kHz and the MASLUW method, exhibiting strong agreement with satellite data below 15 m/s. Designed for compatibility with low-power electronics, the algorithm can be applied to both archival datasets and real-time data streams. It provides a straightforward metric for ocean monitoring and sound source identification. Full article

Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, enhanced by domain-specific prior knowledge. The architecture employs a Convolutional Block Attention Module (CBAM) for localized feature refinement, a lightweight New Transformer Encoder for global context modeling, and a novel TriFusion Block to synergize spectral–temporal–spatial features through parallel multi-branch fusion, addressing the limitations of single-modality extraction. Leveraging the mean teacher framework, DART-MT optimizes consistency regularization to exploit unlabeled data, effectively mitigating class imbalance and annotation scarcity. Evaluations on the DeepShip and ShipsEar datasets demonstrate state-of-the-art accuracy: with 10% labeled data, DART-MT achieves 96.20% (DeepShip) and 94.86% (ShipsEar), surpassing baseline models by 7.2–9.8% in low-data regimes, while reaching 98.80% (DeepShip) and 98.85% (ShipsEar) with 90% labeled data. Under varying noise conditions (−20 dB to 20 dB), the model maintained a robust performance (F1-score: 92.4–97.1%) with 40% lower variance than its competitors, and ablation studies validated each module’s contribution (TriFusion Block alone improved accuracy by 6.9%). This research advances UATR by (1) resolving multi-scale feature fusion bottlenecks, (2) demonstrating the efficacy of semi-supervised learning in marine acoustics, and (3) providing an open-source implementation for reproducibility. In future work, we will extend cross-domain adaptation to diverse oceanic environments. Full article

Accurate prediction of ship underwater radiated noise (URN) during navigation is critical for evaluating acoustic stealth performance and analyzing detection risks. However, the labeled data available for the training of URN prediction model is limited. Semi-supervised learning (SSL) can improve the model performance by using unlabeled data in the case of a lack of labeled data. Therefore, this paper proposes an SSL method for URN prediction. First, an anti-perturbation regularization is constructed using unlabeled data to optimize the objective function of EL, which is then used in the Genetic algorithm to adaptively optimize base learner weights, to enhance pseudo-label quality. Second, a semi-supervised ensemble (ESS) framework integrating dynamic pseudo-label screening and uncertainty bias correction (UBC) is established, which can dynamically select pseudo-labels based on local prediction performance improvement and reduce the influence of pseudo-labels’ uncertainty on the model. Experimental results of the cabin model and sea trials of the ship demonstrate that the proposed method reduces prediction errors by up to 65.5% and 62.1% compared to baseline supervised and semi-supervised regression models, significantly improving prediction accuracy. Full article

Underwater robots require fast and accurate localization results during challenging near-bottom operations. However, commonly used methods such as acoustic baseline localization, dead reckoning, and sensor fusion have limited accuracy. The use of forward-looking sonar (FLS) images to observe the seabed environment for pose estimation has gained significant traction in recent years. This paper proposes a lightweight front-end FLS odometry to provide consistent and accurate localization for underwater robots. The proposed direct FLS odometry (DFLSO) includes several key innovations that realize the extraction of point clouds from FLS images and both image-to-image and image-to-map matching. First, an image processing method is designed to rapidly generate a 3-D point cloud of the seabed using FLS image, enabling pose estimation through point cloud matching. Second, a lightweight keyframe system is designed to construct point cloud submaps, which utilize historical information to enhance global pose consistency and reduce the accumulation of image-matching errors. The proposed odometry algorithm is validated by both simulation experiments and field data from sea trials. Full article

Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network. The framework integrates intelligent computing directly into underwater sensor nodes, employing lightweight AI models to locally classify marine species. Transmitting only classification results, instead of raw data, significantly reduces data volume, thus conserving energy. Additionally, a software-defined radio methodology dynamically adapts transmission parameters such as modulation schemes, packet length, and transmission power to further minimize energy consumption and environmental disruption. GNU Radio simulations evaluate the framework effectiveness using metrics like energy consumption, bit error rate, throughput, and delay. Adaptive transmission strategies implicitly ensure reduced energy usage as compared to non-adaptive transmission solutions employing fixed communication parameters. The results illustrate the framework ability to effectively balance energy efficiency, performance, and ecological impact. This research contributes directly to ongoing development in sustainable and energy-efficient underwater wireless sensor network design and deployment. Full article

Biodiversity monitoring requires the discovery of multi-target tracking. The main requirement is not to reduce the localization error but the continuity of the tracks: a high ratio between the duration of the track and the lifetime of the target. To this end, we present an algorithm for detecting and tracking mobile underwater targets that utilizes reflections from active acoustic emission of broadband signals received by a rigid hydrophone array. The method overcomes the problem of a high false alarm rate by applying a tracking approach to the sequence of received reflections. A 2D time–distance matrix is created for the reflections received from each transmitted probe signal by performing delay and sum beamforming and pulse compression. The result is filtered by a 2D constant false alarm rate (CFAR) detector to identify reflection patterns that correspond to potential targets. Closely spaced signals for multiple probe transmissions are combined into blobs to avoid multiple detections of a single target. The position and velocity are estimated using the debiased converted measurement Kalman filter. The results are analyzed for simulated scenarios and for experiments in the Adriatic Sea, where six Global Positioning System (GPS)-tagged gilt-head seabream fish were released and tracked by a dedicated autonomous float system. Compared to four recent benchmark methods, the results show favorable tracking continuity and accuracy that is robust to the choice of detection threshold. Full article

Phase consistency among hydrophones in fiber-optic hydrophone (FOH) arrays is crucial for effective beamforming. In this study, we investigate the photoelectric detection phase characteristics of FOHs based on the 3 × 3 coupler demodulation technique. We develop a theoretical model combining the 3 × 3 coupler demodulation algorithm with coupled-mode theory to analyze acoustic signal responses. Our model reveals that phase shifts from coupler-to-photodetector and coupler-to-sensing-arm connections arise from different mechanisms, and both are capable of causing π rad phase inversions in demodulated signals. We demonstrate that distinct connection configurations can be classified into groups yielding identical polarity outcomes, and that the input port selection for incident light does not affect output signal phase polarity. Experimental results validate these theoretical predictions. This work establishes critical hardware-level prerequisites for phase polarity consistency in FOH arrays, complementing existing calibration techniques and enhancing array performance in underwater target detection and localization. Full article

The underwater domain presents unique challenges and opportunities for scientific exploration, resource extraction, and environmental monitoring. Autonomous underwater vehicles (AUVs) rely on simultaneous localization and mapping (SLAM) for real-time navigation and mapping in these complex environments. However, traditional SLAM techniques face significant obstacles, including poor visibility, dynamic lighting conditions, sensor noise, and water-induced distortions, all of which degrade the accuracy and robustness of underwater navigation systems. Recent advances in deep learning (DL) have introduced powerful solutions to overcome these challenges. DL techniques enhance underwater SLAM by improving feature extraction, image denoising, distortion correction, and sensor fusion. This survey provides a comprehensive analysis of the latest developments in DL-enhanced SLAM for underwater applications, categorizing approaches based on their methodologies, sensor dependencies, and integration with deep learning models. We critically evaluate the benefits and limitations of existing techniques, highlighting key innovations and unresolved challenges. In addition, we introduce a novel classification framework for underwater SLAM based on its integration with underwater wireless sensor networks (UWSNs). UWSNs offer a collaborative framework that enhances localization, mapping, and real-time data sharing among AUVs by leveraging acoustic communication and distributed sensing. Our proposed taxonomy provides new insights into how communication-aware SLAM methodologies can improve navigation accuracy and operational efficiency in underwater environments. Furthermore, we discuss emerging research trends, including the use of transformer-based architectures, multi-modal sensor fusion, lightweight neural networks for real-time deployment, and self-supervised learning techniques. By identifying gaps in current research and outlining potential directions for future work, this survey serves as a valuable reference for researchers and engineers striving to develop robust and adaptive underwater SLAM solutions. Our findings aim to inspire further advancements in autonomous underwater exploration, supporting critical applications in marine science, deep-sea resource management, and environmental conservation. Full article

The accurate and reliable localization and tracking of Autonomous Underwater Vehicles (AUVs) are essential for the success of various underwater missions, such as environmental monitoring, subsea resource exploration, and military operations. long-baseline acoustic localization (LBL) is a fundamental technique for underwater positioning, but it faces significant challenges in adversarial environments. These challenges include abrupt target maneuvers and intentional signal interference, both of which degrade the performance of traditional localization algorithms. Although particle filter-based Track-Before-Detect (PFTBD) algorithms are effective under normal submarine conditions, they struggle to maintain accuracy in adversarial environments due to their dependence on conventional likelihood calculations. To address this, we propose the BiLSTM-Attention-PFTBD algorithm, which enhances the traditional PFTBD framework by integrating bidirectional Long Short-Term Memory (BiLSTM) networks with multi-head attention mechanisms. This combination enables better feature extraction and adaptation for localizing AUVs in adversarial underwater environments. Simulation results demonstrate that the proposed method outperforms traditional PFTBD algorithms, significantly reducing localization errors and maintaining robust tracking accuracy in adversarial settings. Full article

Predicting the uncertain distribution of underwater acoustic fields, influenced by dynamic oceanic parameters, is critical for acoustic applications that rely on sound field characteristics to generate predictions. Traditional methods, such as the Monte Carlo method, are computationally intensive and thus unsuitable for applications requiring high real-time performance and flexibility. Current machine learning methods excel at improving computational efficiency but face limitations in predictive performance, especially in shadow areas. In response, a machine learning method is proposed in this paper that balances accuracy and efficiency for predicting uncertainties in deep ocean acoustics by decoupling the scene representation into two components: (a) a local radiance model related to environmental factors, and (b) a global representation of the overall scene context. Specifically, the internal relationships within the local radiance are first exploited, aiming to capture fine-grained details within the acoustic field. Subsequently, local clues are combined with receiver location information for joint learning. To verify the effectiveness of the proposed approach, a dataset of historical oceanographic data has been compiled. Extensive experiments validate the efficiency compared to traditional Monte Carlo techniques and the superior accuracy compared to existing learning method. Full article

Distributed Acoustic Sensing (DAS) offers numerous advantages, including resistance to electromagnetic interference, long-range dynamic monitoring, dense spatial sensing, and low deployment costs. We initially deployed a water–land DAS system at the Xinfengjiang (XFJ) Reservoir in Guangdong Province, China, to monitor earthquake events. Environmental noise analysis identified three distinct noise zones based on deployment conditions: periodic 18 Hz signals near surface-laid segments, attenuated low-frequency signals (<10 Hz) in the buried terrestrial sections, and elevated noise at transition zones due to water–cable interactions. The system successfully detected hundreds of teleseismic and regional earthquakes, including a Mw7.3 earthquake in Hualien and a local ML0.5 microseismic event. One year later, the DAS system was upgraded with two types of spiral sensor cables at the end of the submarine cable, extending the total length to 5.51 km. The results of detecting both active (transducer) and passive sources (cooperative vessels) highlight the potential of integrating DAS interrogators with spiral sensor cables for the accurate tracking of underwater moving targets. This field trial demonstrates that DAS technology holds promise for the integrated joint monitoring of underwater acoustics and seismic signals beneath lake or ocean bottoms. Full article

To address the performance degradation of traditional underwater target localization algorithms in high-speed motion scenarios, this paper proposes a vertically moving localization method based on interference feature matching using a single hydrophone. Due to the overlapping of interference fringes in the time–frequency domain, the method first employs a two-dimensional homomorphic filter to separate different types of interference fringes while removing line-spectrum interference. Then, a theoretical interference period database is constructed, and feature matching is performed on the interference fringes to achieve localization of the vertically moving target. Simulation results show that, under conditions of strong broadband noise and line-spectrum interference, the proposed method effectively separates and restores the interference fringes of high-speed vertically moving targets and successfully locates the moving target under large Doppler conditions. Further validation using field data from Qiandao Lake confirms that the actual measurements align with theoretical analysis, validating the effectiveness of the proposed method. Full article