Search Results (368)

Maritime communication systems are among the most important infrastructure of global maritime safety and security. They consist of very high frequency (VHF) radio, the Global Maritime Distress and Safety System (GMDSS), contemporary satellite nets, Automatic Identification System (AIS) networks, and the emerging VHF Data Exchange System (VDES). These systems are essential for distress signaling, navigational coordination, and vessel traffic management. As maritime operations are experiencing accelerated digitalisation, the safety and security dimensions of maritime communication systems have attracted substantial and growing scientific attention. This study presents a comprehensive literature review and bibliometric analysis of the safety and security of maritime communication systems. Guided by the PRISMA 2020 guidelines and Systematic Literature Review (SLR) methodology, a structured search was conducted across three major scientific databases: Scopus, Web of Science (WoS), and IEEE Xplore. Starting from a raw pool of 6648 records retrieved between 2000 and 2026, the dataset was reduced through successive filtering to a final body of 68 high-relevance publications. Bibliometric analysis reveals a significant upward publication trend from 2015 onwards, with a marked acceleration after 2019. Thematic analysis identifies seven principal research clusters: GMDSS modernisation, AIS safety and security, VDES and VHF next-generation systems, maritime cybersecurity, satellite communications, risk assessment frameworks, and emerging technologies, including artificial intelligence and autonomous vessel communications. The review identifies significant research gaps, including the absence of integrated cross-system risk frameworks, insufficient attention to human factors in cybersecurity, limited studies addressing emerging regulatory, legal governance components and a brief analysis of the maritime communications market. This study provides a structured foundation for future research and policy development in maritime communication security. Full article

To address the demand for high-precision, high-efficiency, and standardized hydrographic information in intelligent shipping, this study systematically investigates key technologies for high-resolution hydrographic data parsing and intelligent information services. Focusing on the East China Sea, a space–air–ground integrated monitoring data access system is established. A hybrid data assimilation method combining four-dimensional variational (4D-Var) and ensemble Kalman filter is adopted to realize quality control, deep fusion, and optimal state estimation of multi-source heterogeneous hydrographic observations. A hybrid tidal harmonic response model is further developed to improve the refined forecasting accuracy of tide levels and ocean currents. A hierarchically decoupled system architecture is designed, and modules for data production, sharing, exchange, and visualization are developed in compliance with the international S-100 standard. By integrating hybrid spatiotemporal indexing, multi-level caching, and intelligent query optimization, the system achieves low-latency and high-concurrency service capabilities. Experimental results show that, compared with conventional models, the proposed framework reduces tidal forecast RMSE by approximately 15.8% under extreme weather, raises the continuity index of current vectors to 0.93, and cuts the S-100 product generation latency to less than 30 s. This research establishes a full-chain technical system from data parsing and product generation to intelligent services, providing a reliable technical support platform for ship intelligent navigation, dynamic route planning, and maritime safety assurance. Full article

Large inland waterway infrastructure projects are increasingly exposed to supply disruptions, logistics uncertainty, carbon-control pressure, and dredged-material management challenges. Although resilient supplier selection, closed-loop supply chains, and ESG-oriented optimization have been widely studied, existing models rarely integrate resilient sourcing, hub configuration, forward material supply, reverse dredged-material resourceization, and social externality penalties within a unified maritime infrastructure decision framework. To fill this gap, this study proposes an ESG-endogenous closed-loop supply-chain optimization model for construction of an inland waterway navigation hub. The model jointly optimizes resilient supplier selection, transshipment/resourceization hub activation, equipment deployment, forward material flows, and reverse dredged-material flows. Three objectives are considered: minimizing economic cost, minimizing carbon emissions, and maximizing net social benefit. In particular, a social benefit and ecological-debt penalty function is introduced to quantify the transition from beneficial reuse to disposal-related negative externalities. NSGA-II is adopted as a multi-objective solver, with parameter calibration, convergence analysis, and benchmark comparison used to evaluate computational performance. The Pinglu Canal project is used as a case study. The results produce 14 Pareto-optimal solutions and show that the lowest-cost and lowest-emission configurations may still generate negative social benefits. A low-cost ESG transition region around 197.3–197.8 million CNY is identified, where limited additional investment can activate resourceization pathways and shift the system from ecological debt to near-saturated social benefit. These findings suggest that sustainable infrastructure planning should move beyond isolated cost or carbon minimization and instead identify balanced supplier–hub–equipment–flow configurations that jointly support resilience, circularity, and ESG performance. Full article

With the rapid development of intelligent navigation technologies, accurate dynamic object detection in complex maritime environments remains a critical challenge due to occlusion, scale variation, and multi-target interference. To address these issues, this study proposes a vision–radar fusion-based dynamic object detection method. A cross-modal feature mapping mechanism is developed to achieve deep integration of visual and radar information, and an augmented Lagrangian optimization strategy is introduced to enhance feature consistency and representation capability. Furthermore, an improved Faster R-CNN framework is designed by optimizing the region proposal network and incorporating a multi-scale training strategy to improve detection performance for objects of varying scales. Experimental results on a self-constructed MVRD show that the proposed method achieves detection accuracies of 88.93%, 76.86%, 74.47%, and 83.01% under sunny, strong illumination, foggy, and crossing-waterway conditions, respectively. These results demonstrate that the proposed approach exhibits strong robustness and stability in complex maritime environments. Overall, the method significantly improves dynamic object detection accuracy and provides effective support for reliable environmental perception in intelligent navigation systems. Full article

Autonomy in maritime operations has increased, especially through advancements in Maritime Autonomous Surface Ships (MASS). Despite technical progress, there’s a gap in understanding how human involvement affects safety during transitions between different levels of autonomy. Such transitions are crucial for MASS’s effective operation in mixed fleets of marine vessels across both near-port and open ocean environments. To address this, a hazard analysis framework using System-Theoretic Process Analysis (STPA) is proposed to systematically identify and prioritize critical factors influencing these transitions between human and autonomous agents in marine navigation under different levels of autonomy. The method involves: (1) assessing hazards and operation modes at different autonomy levels; (2) modeling the control structure involving human and autonomous agents; (3) identifying unsafe control actions (UCAs), their causes scenarios and critical factors; and (4) prioritizing critical factors by analyzing triggering events and systemic vulnerabilities from maritime incident reports. We apply this framework to Autopilot (an example of a MASS navigation function) between two ports, reviewing incident reports to uncover UCAs like delayed transition activations, alarm failures, and incorrect initiations by humans or machines. Our findings support the development of targeted safety recommendations to improve MASS navigation operations and are adaptable to other maritime functions. Full article

The rapid development of Maritime Autonomous Surface Ships (MASS) requires advanced data-driven approaches for navigation safety monitoring and human-factor-aware risk analysis. This research proposes a fractal–episodic framework for assessing ship-control microvariability from normalized AIS/ECDIS trajectories in risk-oriented navigation monitoring, with particular relevance to MASS. The framework converts local micro-motion irregularities into passage-level indicators through sliding-window analysis of XTE-derived signals; computation of Higuchi, DFA, and Katz fractal measures; formation of a nine-component track signature; min–max normalization; and weighted aggregation into a chaos score complemented by a confidence index. The proposed framework can support intelligent monitoring and decision-support systems in autonomous maritime operations by providing interpretable behavioral indicators derived from AIS/ECDIS data. Full article

As the core pillar of international trade, the global shipping industry has seen its carbon and pollutant emissions become a key challenge in global environmental governance. Statistics indicate that ship carbon emissions account for 3% of the world’s total anthropogenic CO₂ emissions, while contributing 20% of global NO_x and 12% of SO₂ emissions, posing a serious threat to coastal ecosystems and public health. In response to the International Maritime Organization (IMO) “Net Zero Framework” and national green shipping policies, the transformation of ship power systems toward low-carbon and zero-carbon operation has become an inevitable trend. This paper systematically reviews the research progress and application status of green energy materials for ships, focusing on the working principles, technical characteristics, and engineering application cases of solar photovoltaic (PV) materials, wind energy utilization technologies, fuel cell materials, and alternative clean energy fuels (e.g., liquefied natural gas (LNG), methanol, and hydrogen energy). It also discusses the integration mode and optimization strategy of multi-energy hybrid power systems. The research findings show that solar photovoltaic technology has achieved large-scale application in coastal ships; hydrogen fuel cells are suitable for long-range ocean navigation scenarios due to their high energy density; LNG and methanol have become the current mainstream alternative fuels, relying on mature infrastructure; and hybrid energy systems can significantly improve power supply reliability and emission reduction efficiency through multi-energy complementarity. Finally, aiming at the existing bottlenecks (e.g., cost, energy storage, and safety) of various technologies, future development directions are proposed. This study provides a reference for the technological breakthrough and engineering practice of green energy power systems for ships and contributes to the realization of the “carbon neutrality” goal in the global shipping industry. Full article

This paper investigates the impact of atmospheric conditions on distance determination accuracy in the VDES R-Mode system, based on system development and long-term analytical work conducted within the ORMOBASS project. A dedicated VDES R-Mode transmitter and monitoring station were developed and deployed in Poland, in the Port of Gdynia and at the boatswain’s office in the port of Jastarnia, respectively. Both stations were synchronized in time and frequency using a fiber-optic link and White Rabbit technology, ensuring high-precision and stable operation during long-term measurements. Based on a one-year stationary measurement campaign, a comprehensive dataset combining ranging results and meteorological observations was collected and analyzed. Statistical evaluation demonstrated that atmospheric conditions—particularly rainfall intensity and water vapor density—have a measurable impact on ranging accuracy. These findings motivated the development of a mathematical model describing the relationship between atmospheric conditions and distance measurement errors. The proposed logarithmic regression-based approach was validated using real measurement data; the authors also demonstrated its ability to reduce error variability during changing weather conditions, indicating its potential for future implementation in VDES R-Mode receivers. Full article

Maritime transport is under increasing pressure to cut greenhouse gas and pollutant emissions to meet global decarbonization goals and tighter environmental standards. Ship electric propulsion systems offer a promising solution for short-range maritime operations, particularly for small vessels and coastal activities. Full-electric vessels can significantly reduce operational emissions; however, a key challenge is the extensive charging time for onboard energy storage, which can affect operational continuity and logistical efficiency. This study examines mission planning and energy management for a hybrid multi-source electric mail boat operating in the Aeolian archipelago. It evaluates the viability and performance of a daily inter-island route powered by a high-temperature methanol fuel cell, batteries, and photovoltaic panels. A routing and simulation framework was developed to model the boat’s itinerary among seven islands, accounting for realistic navigation speeds, scheduled stops, solar energy availability, and battery state-of-charge constraints. The study analyzes distance, travel time, energy consumption, solar power generation, and fuel–electric usage with high temporal resolution, enabling detailed analysis of power flows during sailing and docking. Several operational strategies were assessed, including periods of increased speed supported by battery assistance and fuel–electric cell output, combined with coordinated energy management to keep battery levels above a lower acceptable threshold while completing the route in a single day. The methodology provides a practical tool for planning low-emission island networks and supports the integration of innovative energy systems into small electric workboats operating in specific maritime regions. Full article

With the rapid growth of the global shipping industry and the increasing availability of Automatic Identification System (AIS) data, accurate vessel trajectory prediction has become crucial for ensuring navigational safety and optimizing maritime traffic management. This paper presents a systematic review of recent advances in deep learning-based methods for vessel trajectory prediction. We provide a comprehensive analysis of mainstream models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, Sequence-to-Sequence (Seq2Seq) models, and the Transformer architecture. Their performance is compared in terms of spatio-temporal data processing capability, prediction accuracy, and computational efficiency. Furthermore, this review examines practical applications of these methods in scenarios such as collision avoidance and route optimization. Despite notable progress, several challenges remain, including data quality issues, real-time prediction capability, and model interpretability. Future research directions may focus on multi-source data fusion and the development of lightweight model designs to further improve prediction performance. This survey aims to serve as a valuable reference for researchers and contribute to ongoing innovation in vessel trajectory prediction technology. Full article

The Officer on Watch (OOW) is critical to ensuring the safety of the vessel, cargo, and crew during navigation. To reduce maritime accidents caused by unsafe behaviors or fatigue, this paper proposes a dual-branch detection system based on Spatial–Temporal Graph Convolutional Networks (ST-GCN): BODY-ST-GCN for pose-based behavior recognition and FACE-ST-GCN for facial state analysis. For spatial modeling, a Triple Graph Fusion (TGF) strategy is introduced to integrate static, adaptive, and attention graphs, enhancing the representation of skeletal and facial keypoints. For temporal modeling, BODY-ST-GCN incorporates a Three-Scale Parallel Temporal Convolutional Network (TSP-TCN) to capture multi-scale motion dynamics, while FACE-ST-GCN uses a Temporal Adaptive Module (TAM) to extract stable facial state features. Furthermore, a joint risk classification mechanism categorizes OOW duty states into four hierarchical levels: Safe, Early Fatigue Warning, High Fatigue Risk, and Emergency. This mechanism enables continuous, real-time monitoring and dynamic assessment. Experiments demonstrate that BODY-ST-GCN and FACE-ST-GCN achieve macro average precisions of 0.969 and 0.947, respectively, outperforming the baseline ST-GCN by 6.4% and 14.9%, providing reliable technical support for onboard safety management. Full article

Autonomous navigation technologies have been widely adopted in the automotive and aviation sectors, significantly reducing human-error-induced accidents and operational costs. However, their application to maritime systems remains limited due to the complexity of conventional propulsion systems. Electric propulsion ships, with well-defined system boundaries and accessible operational data, offer a promising platform for autonomous navigation. In this study, we propose an FMEA-guided selective multi-fidelity digital twin framework for fault detection, where model fidelity is adaptively selected between low- and high-fidelity models based on risk priority numbers derived from failure mode and effects analysis. This approach enables selective execution of computationally expensive models only under high-risk conditions, thereby improving computational efficiency. In addition, a sliding window-based algebraic aggregation method is employed to achieve lightweight and real-time fault diagnosis. The proposed framework is validated using operational sensor data from a 100 kW electric propulsion ship under multiple fault scenarios, including power supply faults and signal anomalies. Experimental results show that the proposed method reduces computational cost while maintaining stable real-time performance, compared to conventional data-driven AI-based approaches. These results demonstrate that the proposed framework provides an effective and efficient solution for enhancing the reliability and safety of autonomous ship systems. Full article

To address ship-sourced water pollutant issues resulting from shipping industry growth and achieve precise supervision and effective management in coastal ports, this study develops a method for calculating and predicting the generation volume of oily sewage, domestic sewage and solid waste based on Automatic Identification System (AIS) data. First, a questionnaire survey (“Survey on Ship Water Pollutants”) is designed and implemented. Through analysis of questionnaire data, the ranges of values for the generation of oily sewage, domestic sewage, and solid waste from different ship types at China’s coastal ports are established. Additionally, onboard sampling is conducted to determine average emission factors for domestic sewage and oily sewage from typical ship types. Second, ship activities are derived from AIS data and combined with the established generation volume ranges for spatiotemporal calculation. Finally, a ConvLSTM (Convolutional Long Short-Term Memory) model is developed to predict the generation volume of water pollutant based on their spatiotemporal characteristics. Taking a major Chinese port area as a case study, the results indicate that pollutant generation volumes are significant in coastal port zones and main navigation channels, particularly between 15:00 and 16:00. chemical oxygen demand (COD), suspended solids (SS), and 5-day biochemical oxygen demand (BOD₅) levels in domestic sewage exceeded China’s national regulatory limits by 0.35 times, 2.88 times and 1.07 times, respectively, which can easily lead to a decrease in dissolved oxygen content in the water, affecting the respiration and survival of aquatic organisms. Petroleum content in oily sewage remained below the standard threshold. For pollutant generation volume prediction, the proposed ConvLSTM model achieved MAE and RMSE values of 0.0824 and 0.1433, respectively, outperforming other prediction models such as LSTM and CNN-LSTM. This research provides technical support for the prevention and control of water pollution from ships in coastal ports. The proposed AIS-driven framework and ConvLSTM prediction method are transferable and globally applicable, offering a reference for the environmental sustainability of port ecosystems, the global maritime pollution prevention, and the sustainable development of the shipping industry worldwide. Full article

This study addresses the question of how augmented reality (AR) technologies can be designed and integrated into maritime navigation systems to meet the needs of young navigators within contemporary socio-technical bridge environments. The article is based on a qualitative, literature-based research methodology involving a structured analysis and synthesis of peer-reviewed journal articles and conference proceedings related to AR interfaces, human performance, decision support, and maritime training. The reviewed studies indicate that AR can enhance perceptual and situational awareness by overlaying navigational information directly into the navigator’s field of view, thereby reducing head-down time, improving spatial alignment of information, and supporting performance in low-visibility and high-traffic conditions. The literature also shows that AR-enabled visualizations and shared displays can support individual and team-based decision-making by facilitating real-time, context-aware information exchange on the ship’s bridge. Safety-related benefits are identified as indirect outcomes of improved perception and cognitive support rather than as isolated technological effects. Simultaneously, the findings highlight that these benefits depend strongly on human-centered interface design and appropriate training. The study concludes that AR has significant potential to enhance maritime navigation for young navigators when integrated as part of a balanced socio-technical system combining technology, human factors, and structured education. Full article

Accurate vessel speed prediction is essential for maritime traffic supervision, navigational safety, and intelligent coastal management. However, due to the nonlinear, time-varying, and context-dependent characteristics of vessel motion in nearshore waters, conventional single-model approaches often fail to provide sufficiently accurate forecasts. To address this issue, this study proposes a hybrid deep learning framework for Automatic Identification System (AIS)-based nearshore vessel speed prediction and risk warning, integrating a temporal convolutional network (TCN), an attention mechanism, and a bidirectional long short-term memory network (BiLSTM) into a unified architecture. The core novelty of this framework is its task-oriented sequential design, in which TCN extracts local temporal patterns and multi-scale sequence features from historical AIS observations, the attention mechanism adaptively emphasizes informative representations, and BiLSTM models bidirectional contextual dependencies in vessel motion sequences; on this basis, a speed-risk warning process is constructed by combining the predicted speed with electronic-fence threshold constraints. Experiments conducted on real AIS data from coastal waters show that the proposed method obtains lower mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) as well as a higher coefficient of determination (R²) than several benchmark models. The results illustrate that the proposed framework effectively improves vessel speed prediction accuracy within the studied coastal area and provides practical support for proactive maritime supervision and nearshore safety management. Full article