Search Results (191)

Offshore wind turbine operation and maintenance in complex sea states is influenced by the coupled effects of low-frequency vessel drift and high-frequency wave-induced disturbances. In practical operations, the ship dynamic positioning system primarily regulates low-frequency motion through vessel position control, whereas a boarding compensation system is required to attenuate high-frequency six-degrees-of-freedom motions to ensure safe personnel transfer. This study establishes coupled kinematic mapping among the ship dynamic positioning system, the Stewart platform, and a three-degrees-of-freedom gangway and proposes a hierarchical cooperative control architecture. At the upper layer, an extended Kalman filter and an exponential moving average low-pass filter are employed for online state estimation and for separating low-frequency and high-frequency components. A Kalman filter lookahead predictor is then used to generate a short-horizon prediction of the high-frequency component and to construct a feedforward reference signal. At the middle layer, the feedforward reference and the gangway end error feedback are coordinated at the velocity level, and a quadratic programming-based allocation strategy distributes compensation tasks between the Stewart platform and the gangway under safety-related constraints, including actuator stroke limits and singularity avoidance. At the lower layer, a robust feedback controller is designed for the gangway to mitigate modeling uncertainties and environmental disturbances and to ensure stable tracking. MATLAB R2024a-based simulations under representative wave conditions demonstrate that the proposed architecture improves end effector tracking accuracy and closed-loop stability compared with baseline strategies, providing a feasible engineering solution for shipboard boarding operations in complex sea states. Full article

In response to the problem that sensors cannot be directly installed at key local positions on the surface of ship hull structures during the transient strong shock process of underwater explosions due to spatial constraints or large plastic deformations, this paper investigates the chaotic-like nonlinear transient behavior of structural dynamic response systems under strong shock and proposes a key position structural response reconstruction method based on dynamic inversion. Since the structural response under a transient strong shock exhibits significant non-stationarity and nonlinearity, signals from neighboring measurement points cannot directly characterize the dynamic behavior at key positions. Therefore, the shock response signals are discretized in both time and space dimensions. The phase space reconstruction method is employed to characterize the motion trajectory of acceleration responses in a two-dimensional phase space, establish mapping functions for system motion evolution, and use their control parameters to characterize the system’s nonlinear dynamic behavior. Furthermore, based on the spatiotemporal dynamic equations, a spatiotemporal coupled mapping model for spatial state points is established to achieve the theoretical inversion of acceleration responses at key positions. This method provides theoretical support for analyzing the dynamic characteristics of structures at key positions under strong shock environments, characterizing the shock environment, and assessing and designing equipment for shock safety. However, the current validation is based on high-fidelity numerical simulations rather than physical prototype tests; therefore, the predictive capability of this method in actual physical environments requires further validation through subsequent physical model tests. Full article

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring. Full article

Aiming at the divergence problem of standalone strapdown inertial navigation system (SINS) affected by initial errors, sensor drift, and cumulative errors in complex marine environments, this paper proposes a long-endurance autonomous navigation scheme without external measurement to suppress Schuler oscillations and improve dynamic navigation performance. First, based on the dynamic error model of SINS, the characteristics of Schuler oscillation are analyzed, and a multi-path delayed-solution strategy is developed. By sequentially delaying the SINS calculation loop and performing arithmetic averaging, periodic oscillation errors are automatically canceled. Second, a chi-square test is constructed to assess sea-state complexity in real time, and a robust adaptive Kalman filter is designed with adaptive filter selection to further improve estimation accuracy under dynamic conditions. Finally, the proposed method is systematically validated through static simulations, dynamic simulations, and full-scale ship experiments. Results show that the delayed-solution strategy significantly mitigates Schuler oscillation in attitude and velocity under static conditions. In dynamic simulations and ship trials, compared with pure SINS, single delayed-calculation, and conventional Kalman filter, the proposed approach achieves superior suppression of attitude, velocity, and position errors, with core navigation error indices reduced by at least one order of magnitude. These findings demonstrate that the Schuler period characteristic of inertial navigation errors can be effectively exploited in dynamic conditions, and the coupling of multi-path delayed calculation with robust adaptive filtering enables substantial improvements in autonomous navigation accuracy without external measurement. The proposed method expands the theoretical and engineering framework of autonomous navigation at no additional hardware cost, providing a new technical route for the practical deployment of long-duration SINS. Full article

The navigation of ships in complex shallow water areas is constrained by various factors such as water depth, channel boundaries, and environmental interference. Therefore, it is crucial to improve the adaptability and effectiveness of collision avoidance decisions for ships in complex shallow water scenarios. To address these issues, this paper proposes a multi-factor constrained autonomous decision-making method for complex shallow water vessel maneuvering. Firstly, a digital transportation environment was constructed by combining dynamic and static information, such as water depth, tides, channel boundaries, changes in maneuvering characteristics, and navigation rules, and a navigable water area model that was suitable for shallow water was proposed. Then, considering the constraints of ship maneuverability and the navigation environment, a shallow water ship motion model affected by wind flow was developed. A complex shallow water adaptive maneuvering coupled decision-making method was constructed, considering the influence of ship navigation rules and channel constraints. This method utilizes the Kalman filtering algorithm to correct residuals and predict the maneuvering of the target vessel. Integrated improved heading control and guidance algorithms achieved automatic heading control and future position prediction. Through testing and verification in the complex waters of the Yangtze River estuary, the results show that the autonomous collision avoidance decision-making method proposed in this paper can effectively make collision avoidance decisions in complex multi-ship shallow water areas. This study can provide innovative and practical solutions for the technological development of autonomous ship collision avoidance decision-making. Full article

To address the lag and limited sensitivity of conventional shipping freight indicators, this study develops a news-based sentiment measure for the shipping market and examines its association with BDI dynamics. Using shipping-related news headlines from 2019 to 2025, a RoBERTa classifier fine-tuned on manually annotated data is used to quantify headline sentiment, and a daily Cumulative Sentiment Index (CSI) is constructed using an event-smoothing window with exponential decay. A higher CSI indicates more positive market sentiment, while a lower CSI reflects more negative sentiment. Empirical evidence shows that CSI exhibits pronounced responses around major market events and is closely linked to BDI behavior in event windows. In addition, an EGARCH specification augmented with CSI indicates that sentiment is significantly associated with conditional volatility, suggesting that news-based sentiment contains incremental information relevant to BDI risk dynamics. Overall, the proposed CSI provides a quantitative approach to tracking shipping market sentiment using publicly available news headlines and offers a complementary perspective to transaction-based freight indices. Full article

Detecting ships using Synthetic Aperture Radar (SAR) in coastal areas is still difficult due to the impact of coherent speckle noise from the ocean surface, complex land clutter and having multi-scale target representations in the radar imagery. Most of the existing ship detection algorithms lose important target features during downsampling and have difficulty recovering those features through upsampling, resulting in a high number of false detections and missed detections. In this work, we present a new ship detection algorithm called Synergistic Star-Attentive Network with Dynamic Feature Refinement (S²D-Net). First, we create a new backbone called Multi-scale PCCA-StarNet to generate robust feature representations. Within the backbone we implement a Progressive Channel-Coordinate Attention (PCCA) mechanism to create a synergy between global channel filtering and adaptive coordinate locking to decouple ship textures from granular speckle noise. Second, we create a Dynamic Feature Refinement Neck. We develop a content-aware dynamic upsampler called DySample to replace conventional interpolation to improve fidelity of the upsampled feature of small targets. Further, we design a Star-PCCA Feature Aggregation module which fuses features together. Using star-operations and the PCCA mechanism, this module refines semantic features and removes background clutter while aggregating features across multiple scales. Third, we develop a Lightweight Shared Convolutional Detection Head with Quality Estimation (LSCD-LQE). The LSCD-LQE decreases parameter redundancy by using shared convolutional layers and adds a localization quality estimation branch. Therefore, the LSCD-LQE effectively reduces false positive detections through alignment of classification scores with localization quality based on Intersection over Union (IoU) in difficult coastal environments. Our experimental results, using the SSDD and HRSID datasets, show that S²D-Net produces results comparable to representative ship detection algorithms. In particular, on the challenging HRSID inshore subset, our proposed method achieved a mean average precision (mAP) of 82.7%, which is 6.9% greater than the YOLOv11n baseline ship detection algorithm. These results demonstrate that S²D-Net is superior at detecting small coastal vessels and mitigating the detrimental effects of the nearshore complex environment on the performance ship detection using SAR. Full article

Using computational fluid dynamics (CFD) coupled with the volume of fluid (VOF) method, we developed an analytical framework to quantify free-surface suction around ship hulls. The DTMB 5415 benchmark hull was employed to investigate the mechanisms by which underwater tail fins influence surface wake dynamics. We systematically evaluated the effects of tail-fin span on hydrodynamic drag and free-surface suction across the investigated speed range. Within the Froude number range of 0.05–0.45, underwater tail fins reduced air entrainment by optimizing hull attitude and attenuating stern waves. Free-surface suction capacity exhibited a positive correlation with vessel speed and a negative correlation with tail-fin span length. At Fr = 0.45, the free-surface suction capacity of the bare hull was 13.78 times greater than that at Fr = 0.15. At this speed, the L₄ tail-fin configuration achieved a 13.292% reduction in free-surface suction. In contrast, the L₂ tail-fin configuration provided a suction reduction of only 9.98%. The optimal tail-fin span represents a trade-off between drag reduction and wake suppression, as longer spans do not necessarily yield superior performance. Under cruise conditions (Fr = 0.25–0.35), the L₂ tail-fin configuration exhibited optimal performance, achieving a 5.292% reduction in drag and a 13.492% reduction in free-surface suction. Across the tested Froude number range of 0.05–0.45, underwater tail fins simultaneously improved hydrodynamic performance and reduced free-surface suction, thereby effectively suppressing bubble wake formation. Full article

To address dynamic positioning (DP) challenges encountered by ships navigating amid unknown model parameters, environmental disturbances, and input saturation, this study proposes a nonlinear gains recursive sliding mode (RSM) DP control law. Within this control framework, an RSM strategy is devised, leveraging variable-gain technology to enhance DP system control performance. A variable-gain adaptive radial basis function (RBF) neural network is employed for real-time online training to approximate the unknown ship model. Simultaneously, an auxiliary dynamic system is incorporated to deal with input saturation. Furthermore, a robust control item is implemented to counteract the influence of RBF neural network approximation errors and external disturbances on the DP system. By constructing an appropriate Lyapunov function, it is proven that all signals in the DP closed-loop control system are uniformly ultimately bounded. Finally, simulation results demonstrate the ship DP system’s rapid response and high accuracy under the proposed control law, along with an enhanced ability to reject environmental disturbances. Full article

The escalating development of Maritime Autonomous Surface Ships (MASS) has imposed rigorous demands on the precision, continuity, and resilience of onboard integrated navigation systems. However, in complicated marine settings, data from the Global Navigation Satellite System (GNSS) and Doppler Velocity Log (DVL) are frequently corrupted by multipath effects and non-line-of-sight (NLOS) interference. These disturbances introduce anomalous observations that violate Gaussian noise assumptions, thereby severely deteriorating the robustness and estimation quality of traditional sliding-window factor graph optimization (SW-FGO). To mitigate this problem, this study introduces a novel integrated navigation strategy termed gradient-adaptive factor graph optimization (GA-FGO). By designing a gradient-adaptive robust objective function within the factor graph structure, the proposed method dynamically re-weights constraints from the inertial navigation system (INS), GNSS, and DVL. This mechanism adequately suppresses the influence of measurement outliers at the optimization level. Furthermore, a unified solution framework utilizing iterative reweighted least squares (IRLS) and the Gauss–Newton method is established to simultaneously perform adaptive weight updates and state estimation. Validation was based on offline field data benchmarked against the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and standard SW-FGO. The simulation results demonstrated that the GA-FGO algorithm achieves superior positioning accuracy and estimation stability under realistic measurement conditions. Full article

Reliable object detection in marine radar imagery is critical for maritime situational awareness, collision avoidance, and autonomous navigation. However, it remains challenging due to sea clutter, small targets, and interference from fixed navigational aids. This study proposes a high-precision detection pipeline that integrates tiled training, Sliced Aided Hyper Inference (SAHI), and an oriented bounding box (OBB) variant of the lightweight YOLOv11 architecture. The proposed approach effectively addresses scale variability in Plan Position Indicator (PPI) radar images. Experiments were conducted on the real-world DAAN dataset provided by the German Aerospace Center (DLR). The dataset consists of 760 full-resolution radar frames containing multiple moving vessels, dynamic own-ship, and clutter sources. A semi-automatic contour-based annotation pipeline was developed to generate multi-format labels, including axis-aligned bounding boxes, oriented bounding boxes (OBBs), and instance segmentation masks, directly from radar echo characteristics. The results demonstrate that the tiled YOLOv11n-OBB model with SAHI achieves an mAP@0.5 exceeding 0.95, with a mean center localization error below 10 pixels. The proposed method shows better performance on small targets compared to standard full-image baselines and other YOLOv11 variants. Moreover, the lightweight models enable near real-time inference at 4–6 FPS on edge hardware. These findings indicate that OBBs and scale-aware strategies enhance detection precision in complex marine radar environments, providing practical advantages for tracking and navigation tasks. Full article

To address the problem of insufficient detection accuracy, high false negative rate of small targets, and large positioning errors of ships in complex marine environments and foggy conditions, an improved DBL-YOLO method based on YOLOv11 is proposed. This method customizes and optimizes modules according to the characteristics of foggy scenes—the C3k2-MDSC module is designed to efficiently extract and fuse multi-scale spatial features, and a dynamic weight allocation mechanism is adopted to balance the contributions of features at different scales in the foggy and blurred environment; a lightweight BiFPN structure is introduced to enhance the efficiency of cross-scale feature transmission and solve the problem of feature attenuation in foggy conditions; a novel fusion of the Deformable-LKA attention mechanism is innovated, which combines a large receptive field and spatial adaptive adjustment capabilities to focus on the key contour features of blurred ships in foggy conditions; an Inner-SIoU regression loss function is proposed, which optimizes the positioning accuracy of dense and small targets through an auxiliary bounding box dynamic scaling strategy. Experimental results show that in foggy scenes, the recall rate is increased by 3.4%, the F1 score is increased by 1%, and mAP@0.5 and mAP@0.5:0.95 are increased by 1.4% and 3.1%, respectively. The final average precision reaches 98.6%, demonstrating excellent detection accuracy and robustness. Full article

Coastal zones, as critical ocean–land–atmosphere ecotones, face significant ecological threats from persistent organic pollutants like organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs). However, there are still obvious deficiencies in the understanding of the pollution characteristics and ecological risks of OCPs and PCBs in the coastal environment of South China, especially in western Guangdong. Due to the absence of prior research on these pollutants in the Maoming area, we measured the grain sizes from 157 sediment samples and the concentrations of PCBs and OCPs from 11 key locations to assess their environmental occurrence and risks. As analyzed by the GC-MS system, OCP levels range from 0.39 to 50.20 ng/g (mean 10.25 ng/g), while PCB concentrations range from 1.6 to 92.59 ng/g. Through the analysis of pollutant data and analysis of similar areas, we found that OCPs and PCBs in the Maoming coastal zone primarily originate from fishing port operations, ship antifouling paints, and historical legacy pollutants. In addition, the distribution of pollution is significantly controlled by hydrodynamic conditions and the semi-enclosed geomorphological characteristics of the bay. As grain size increases, the correlation with pollutant concentrations shifts from positive to negative. This trend reveals that finer-grained sediments in low-energy environments accumulate significantly higher levels of pollution compared to their coarser counterparts in more dynamic settings. Compared to other coastal regions globally, the study area demonstrates relatively lower pollution intensity. Dual assessments using Sediment Quality Guidelines (SQGs) and Sediment Quality Standards (SQSs) indicate a generally low probability of adverse biological effects, with elevated risk localized to sites near port activities. This study provides a scientific basis for the prevention and control of OCP and PCB pollution in the Maoming coastal zone and also provides a reference for pollution assessment in similar areas. Full article

Accurate wave detection is essential for reliable ship motion prediction and the safety of offshore operations. Wave buoys are widely deployed as key instruments for capturing wave characteristics. However, buoys drift due to the waves and currents, resulting in errors in reconstructed wave elevation. To address this challenge, a fixed-position wave-elevation reconstruction method is proposed in this paper. First, a temporal convolutional network (TCN) module is integrated with a gated recurrent unit (GRU) network to efficiently capture the nonlinear relationship between buoy motion and wave elevation, enabling simultaneous wave elevation reconstruction and dynamic deviation compensation. Second, a static deviation compensation algorithm developed from wave theory is introduced to convert the spatial deviation into temporal misalignment. The proposed method is evaluated in both time and frequency domains across various sea conditions. Results demonstrate that the proposed method effectively compensates for deviations and achieves accurate reconstruction of wave elevation at the target position. In higher sea states, accurate reconstruction is maintained even at large static deviations, with relative errors typically within 10–15%. Frequency-domain analysis shows that coherence approaches 1 near the spectral peak and below 0.3 at higher frequencies, indicating that the dominant wave components are accurately reconstructed and that high-frequency noise has a limited impact on overall accuracy. Full article

Long-term wind speed changes over the Yangtze River waterway have critical implications for inland shipping efficiency, emission dispersion, and renewable energy potential. This study utilizes a high-resolution 5 km gridded reanalysis dataset spanning 1979–2018 to conduct a comprehensive spatiotemporal analysis of surface wind climatology, variability, and trends along China’s primary inland waterway. A pivotal regime shift was identified around 2000, marking a transition from terrestrial stilling to a recovery phase characterized by wind speed intensification. Multiple change-point detection algorithms consistently identify 2000 as a pivotal turning point, marking a transition from the late 20th century “terrestrial stilling” to a recovery phase characterized by wind speed intensification. Post-2000 trends reveal pronounced spatial heterogeneity: the upstream section exhibits sustained strengthening (+0.02 m/s per decade, p = 0.03), the midstream shows weak or non-significant trends with localized afternoon stilling in complex terrain (−0.08 m/s per decade), while the downstream coastal zone demonstrates robust intensification exceeding +0.10 m/s per decade during spring–autumn daytime hours. Three distinct wind regimes emerge along the 3000 km corridor: a high-energy maritime-influenced downstream sector (annual means > 3.9 m/s, diurnal peaks > 6.0 m/s) dominated by sea breeze circulation, a transitional midstream zone (2.3–2.7 m/s) exhibiting bimodal spatial structure and unique summer-afternoon thermal enhancement, and a topographically suppressed upstream region (<2.0 m/s) punctuated by pronounced channeling effects through the Three Gorges constriction. Critically, the observed recovery contradicts widespread basin greening (97.9% of points showing significant positive NDVI trends), which theoretically should enhance surface roughness and suppress wind speeds. Correlation analysis reveals that wind variability is systematically controlled by large-scale atmospheric circulation patterns, including the Northern Hemisphere Polar Vortex (r ≈ 0.35), Western Pacific Subtropical High (r ≈ 0.38), and East Asian monsoon systems (r > 0.60), with distinct seasonal phase-locking between baroclinic spring dynamics and monsoon-thermal summer forcing. These findings establish a comprehensive, fine-scale climatological baseline essential for optimizing pollutant dispersion modeling, and evaluating wind-assisted propulsion feasibility to support shipping decarbonization goals along the Yangtze Waterway. Full article