Abstract One of the most important problems in maritime navigation is that of finding the ship’s position at sea. Nowadays, the ship’s position can be determine using Global Navigation Satellite Systems (GNSSs); however, celestial navigation methods are still taught and can be used as a second line method in case the GNSS fails. In this paper, we present a method proposed by Carl Gauss, in 1809, to determine the ship’s position at sea. The method proposed requires only the observation of two altitudes of the Sun at two different instants of the day and calculates the geographical position of the ship without the need of other information such as assumed position or the use of the Nautical Almanac. This self-contained method was implemented in an Excel spreadsheet and was tested using real observations of the Sun. The results obtained were compared with the GPS (Global Positioning System) coordinates and we concluded that the results are extremely satisfactory. The method proposed represents a good alternative when the electronic systems fail.
Safe navigation of maritime autonomous surface ships (MASS) relies on two capabilities: path planning and collision avoidance. This review surveys classical algorithms and modern AI techniques for embedding the International Regulations for Preventing Collisions at Sea (COLREGs) into autonomous navigation. We organise prior work into three families—classical search/optimisation, real-time reactive methods, and learning-based approaches—and discuss their strengths and limitations with respect to rules compliance, computational cost, and onboard constraints. Building on these insights, we outline a large-language-model framework, Navigation-GPT, which couples reasoning-and-acting (ReAct) prompting with low-rank adaptation (LoRA). We further propose a three-phase deployment roadmap for MASS: core model integration, domain fine-tuning, and integrated operations. The paper concludes with open challenges and research directions toward reliable, explainable, and fully compliant MASS navigation.
In maritime transportation, pilotage plays a crucial role in ensuring navigational safety. Marine pilots possess in-depth knowledge of local waters, currents and weather conditions, guiding ships safely through complex waterways. This expertise minimises risks such as collisions and groundings, thereby protecting both the ship and the environment, and promoting safe, efficient maritime traffic management. However, grounding accidents in pilotage waters can still lead to severe environmental, economic and operational damage, including oil spills, ecosystem harm and costly salvage efforts. Continuous improvements in pilotage are therefore vital to minimise these risks. In this study, with the combination of HFACS methodology and Apriori algorithm, factors contributing to grounding accidents in ships navigating with marine pilots and ship features are examined, and strong association rules among factors are achieved. The prominent factors discovered are 'Ship-Marine Pilot Communication Problems', 'Inappropriate Passage Plan', 'Ineffective Usage of Bridge Equipment', 'Port Authority Resource Management' and 'Ineffective Teamwork'. Ship-marine pilot communication problems are the most prevalent factor in these derived rules which is appearing in 8 of 9 rules and exerting a substantial influence on the accidents. Inappropriate passage planning, identified in 6 rules, emerges as another significant and recurrent contributing factor. Based on the association rules, this study provides significant insights and actionable recommendations for stakeholders to prevent grounding accidents in marine pilot-assisted navigation.
Underwater acoustic source localisation is essential for marine monitoring, navigation of autonomous underwater vehicles and underwater surveillance. Time Difference of Arrival (TDOA) localisation is attractive because it avoids absolute time synchronisation; however, its accuracy degrades in realistic underwater channels due to multipath, measurement noise and environmental variability (e.g. sound-speed mismatch) as well as sensor geometry limitations. This paper proposes an optimisation-based TDOA localisation framework that integrates: (i) Kalman filtering (KF) for dynamic tracking; (ii) extended Kalman filtering (EKF) to handle nonlinear measurement models; and (iii) nonlinear least-squares (NLS) minimisation to refine the source position. A parametric analysis is also presented by varying key system parameters - primarily noise level and measurement uncertainty - to quantify performance trade-offs in terms of localisation error and convergence behaviour. Simulation results (static and moving source cases) show that LS provides high accuracy for low-noise/static cases, while KF/EKF are more robust for dynamic and high-noise scenarios; EKF achieves the fastest error decay due to explicit nonlinear modelling. These results demonstrate the proposed framework's effectiveness for robust underwater acoustic source localisation.
This paper focuses on developing a robust control strategy for robotic manipulators, which are widely used in industrial and automation systems due to their flexibility and precision. However, their performance is often affected by factors such as unmodelled dynamics, friction and external perturbations, making accurate trajectory tracking a challenging task. To address these issues, an enhanced active disturbance rejection control (EADRC) scheme is proposed. The method employs an extended state observer (ESO) based on the system dynamics to estimate and compensate for internal uncertainties and external disturbances in real time. To boost the tracking accuracy, a nonlinear feedback control scheme is formulated. To further refine its performance, key controller parameters are tuned using an enhanced particle swarm optimisation (PSO) method, which incorporates elements of chaos theory to improve global search capability and convergence behaviour. The proposed EADRC method is evaluated through comparison with conventional controllers, and the results demonstrate its superior tracking accuracy and robustness.
To address the challenges of long voyages and the significant effects of Earth's curvature on ocean navigation, this paper proposes, for the first time, a guidance and control strategy for great-circle routes based on Mercator projection nautical charts. First, a guidance strategy for great-circle routes is designed by combining the traditional line-of-sight (LOS) algorithm with spherical triangles. Tracking control is subsequently achieved through the integration of a closed-loop gain-scheduling algorithm. Next, the vessel's position is transformed from a planar map to a Mercator projection nautical chart to better meet the practical needs of maritime engineering. Finally, the effectiveness of the designed guidance and control algorithm is verified through simulations. The experimental results show that the proposed guidance and control strategy can significantly enhance the stability of the vessel along the great-circle route, reduce navigation time and lower fuel consumption, demonstrating high navigation efficiency and economy.
Ship path planning represents a fundamental challenge in intelligent navigation, requiring careful balance between route optimality, safety in complex marine environments. To address the limitations of conventional A* algorithms, this paper proposes an improved multi-factor and multi-scale A* algorithm. The methodology begins with processing ENC data, where canny edge detection combined with adaptive thresholding constructs obstacle maps. A novel dual-layer multi-scale grid framework is established: They are used to rapid global path searching, and precise collision avoidance. The algorithm innovatively integrates a multi-factor function that simultaneously considers obstacle distribution, environment effects, navigation rules, and ship dynamic constraints, with adaptive weight adjustment optimizing the search process. Path refinement employs smoothing algorithms to significantly reduce waypoint numbers. Simulation experiments conducted in Dalian port demonstrate the algorithm's superior performance: maintaining safe clearance even in obstacle-dense areas and using the shorter length. Experimental results confirm that generated paths better satisfy practical navigation requirements.
Accurate vessel traffic prediction is significant for efficient waterway management and lock scheduling. This paper presents a deep-learning framework that integrates a multi-graph convolutional network with a gated recurrent unit network, considering spatio-temporal patterns appropriately, for vessel traffic flow prediction. Three unstructured graphs are constructed to represent spatio-temporal relationships among traffic flows at different locations. Subsequently, multi-graph convolution is employed to quantitatively extract such patterns among adjacent nodes in the graphs. Those extracted patterns are then passed to a gated recurrent unit layer for further temporal features extraction in sequential data. The model is believed to improve prediction accuracy and reliability. To prove this, extensive experiments on regional and station-based predictions are conducted using two real-world datasets to evaluate the model's capability. The jointly trained model demonstrates superior performance and outperforms conventional methods. The strong forecasting ability enables managers to adjust schedules promptly, enhancing efficiency and intelligence of waterway operations.
Given the pace of port digitalisation, this study provides a mapping of the smart port cybersecurity literature to clarify its intellectual structure and emerging research directions. Bibliographic records period 2010-2025 were retrieved from the Scopus and analysed Bibliometrix/Biblioshiny. The dataset comprises 460 publications from 344 sources, with an annual growth rate of 11.02% and an average of 9.97 citations per article, indicating a expanding research domain. The analysis examines publication trends, co-authorship and citation networks, and conceptual structures through bibliometric and text-mining techniques. Results show a sharp increase in publications after 2017, driven by the integration of the Internet of Things (IoT), artificial intelligence and automation in port systems. International collaboration is prominent, with research leadership concentrated in the USA, China, India and the UK. Conceptual analysis highlights network defence, intrusion detection and AI-based security, while revealing gaps at the intersection of governance, cyber-physical resilience and operational security in smart ports.
Maritime transport plays a vital role in global logistics and trade; however, its environmental impact, particularly CO2 emissions, has become a growing concern. Current estimation methodologies are divided into top-down and bottom-up approaches. Top-down methods rely on macro-statistical data but often lack specificity regarding individual ship characteristics, leading to high uncertainty. Bottom-up methods, increasingly prevalent due to advancements in ship equipment and big data technology, estimate CO2 emissions based on detailed ship activity trajectories, offering greater precision. This study integrates data from multiple vessel-position transmitting devices - AIS, V-Pass, and LTE-Maritime - to estimate CO2 emissions from maritime activities in the coastal regions of South Korea. By combining these data sources, the study develops a comprehensive and accurate emissions assessment, improving reliability and supporting more informed decision-making in maritime environmental management and policy development.
Adapting Barker’s ((2019). The Journal of Navigation , 72 (3), 539–554) taxonomy of wayfinding behaviours – originally developed for man-made environments, paper and screen – we examined which behaviours are also found in the outdoors. In the analysis of the collected data from a questionnaire ( n =401), we find that participants employ every category in Barker’s framework of social, semantic and spatial behaviours. Our respondents report the use of digital maps on a mobile phone as the most common behaviour, with following directional signs as the second most used. Furthermore, social wayfinding behaviours figure prominently and the participants express preferences for various information sources. We demonstrate similarities of behaviours across the different types of environments and we confirm the applicability of Barker’s taxonomy of wayfinding behaviours also in nature. Our study generates knowledge that potentially can make navigation simpler and more efficient through wayfinding design, and lead to heightened feeling of safety in the outdoors. Wayfinding behaviour studies, like this one, can serve as a bridge between human psychology and practical design.
Maritime safety faces growing challenges due to an expanding global fleet, tighter schedules, and increasingly complex stakeholder interactions. This study integrates multiple data sources to determine a more accurate representation of major marine accident causative factors in the United Kingdom. Logistic regression and data modelling are applied to Automatic Identification System data (2011–2017) and reported accidents from the Marine Accident Investigation Branch (2013–2019). Results show that larger vessels, daytime transits, service ships, winter conditions, and confined high-density areas such as ports impact accident likelihood. Interviews validate the data and emphasize the influence of port geometry and channel complexity. Among major UK ports, London, Plymouth and Milford Haven exhibit the highest accident-to-traffic densities. While maritime regulations and safety management systems in ports and vessels are seen as adequate by industry professionals, human factors require the greatest attention to improve maritime safety.
The paper explores the accuracy of WiFi-Round Trip Timing (RTT) positioning in indoor environments. Filtering techniques are applied to WiFi-RTT positioning in indoor environments, enhanced by Residual Signal Strength Indicator (RSSI)-based outlier detection. A Genetic and Grid filter are compared with a Particle filter and single-epoch least-squares across a range of test scenarios. In static scenarios, 67% of trials had sub-metre accuracy and 90.5% had a root mean square error (RMSE) below 2 m. In Non-Line-of-Sight (NLOS) conditions, 38% of trials had sub-metre accuracy, whereas for environments with full Line-of-Sight (LOS) conditions, 95.2% of trials had sub-metre accuracy. In scenarios with motion, 22.2% of trials had sub-metre accuracy. RSSI-based outlier detection in NLOS conditions, provided an average improvement of 41.3% over no outlier detection across all algorithms in the static and 14% in the dynamic tests. The Genetic filter achieved a mean improvement of 49.2% in the static and 47% in the dynamic tests compared with least squares.
The unmanned surface vehicle (USV) is deemed with significant potential to deal with the maritime search and rescue (SAR) missions. This paper investigates the path planning of the USV with SAR tasks, and proposes a novel algorithm based on combined convolutional neural network rapid-exploration random tree and improved artificial potential field (CRRT-IAPF). The proposed scheme can be divided into the global and the local path plannings. The rapid-exploration random tree (RRT) method is employed to generate the global path in the sea chart, which is further discriminated to be optimal or non-optimal through a well-trained convolutional neural network (CNN). The artificial potential field (APF) method is adopted to plan the local path in the environment with small obstacles and SAR task points. To facilitate the path convergence and avoid the oscillation, the potential field function is improved in a more efficient way. In addition, the evaluation functions of search success rate and rescue success rate are established to evaluate the completeness of SAR tasks. Through the simulation, it is verified that the proposed CRRT-IAPF scheme has the superiority over the others.
During a regatta, the influence of wind speed on the velocity of the boat, the distance covered and the manoeuvres carried out has not been clarified to date in the 49er and 49erFX classes. Therefore, the main aim of this study was to analyse how these variables are affected by wind speed during a regatta. The sample consisted of 39 Olympic sailors from the 49erFX and 49er classes, who participated in a World Cup. Velocity, velocity made good (VMG), distance and manoeuvres were evaluated in the upwind and downwind legs using global positioning system (GPS) devices. In both classes, it was observed that mean velocity, VMG and distance travelled increased as the wind velocity increased in upwind and downwind legs. The velocity, the distance travelled and the manoeuvres carried out are conditioned by wind speed in both upwind and downwind legs in the 49er and 49erFX classes.
The need for Global Navigation Satellite System (GNSS) receiver testing increases with the advent of widespread Internet of Things (IoT) technologies and other electronic devices dependent on position determination. In this paper, a low-cost GNSS multiband L1+L5 signal recorder and replayer for equipment testing purposes is proposed. It is implemented using Software-Defined Radio (SDR) modules HackRF One with proper time and phase synchronisation. The recorder-replayer has been tested with GPS, GALILEO, BEIDOU and GLONASS satellites and several commercial GNSS receivers. Reduced GNSS signal bandwidth of approximately 10 MHz is sufficient for efficient reception of recorded signals. Performed tests with a driving car show applicability of this GNSS recorder-replayer in dynamic settings.
This paper analyses the performance of the Australian and New Zealand Satellite-Based Augmentation System (Aus-NZ SBAS) test-bed to evaluate its use in civil aviation applications with a focus on dual-frequency multi-constellation (DFMC) signals. The Aus-NZ SBAS test-bed performance metrics were determined using kinematic data recorded in flight across a variety of environments and operational conditions. A total of 14 tests adding up to 32 h of flight were evaluated. Flight test data were processed in both the L1 SBAS and DFMC SBAS modes supported by the test-bed broadcasts. The performance results are reviewed regarding accuracy, availability and integrity metrics and compared with the requirement thresholds defined by the International Civil Aviation Organisation (ICAO) for Precision Approach (PA) flight operations. The experimentation performed does not allow continuity assessment as specified in the standard due to a long-term statistical requirement and inherent limitations imposed by the reference station network. Analysis of flight test results shows that DFMC SBAS provides several performance improvements over single-frequency SBAS, tightening both horizontal and vertical protection levels and resulting in greater service availability during the approach.
Maritime navigation in low visibility presents a significant challenge, jeopardising seafarers' situational awareness and escalating collision risks. This study introduces a maritime head-up display (mHUD) to address this issue. The mHUD, a 2-m diameter aluminium ring with dual rows of LEDs, enhances visibility for autonomous ships in adverse conditions on ship bridges and remote operating centres (ROCs). Displaying various modes such as shallow waters, land, lighthouses, beacons, buoys and maritime traffic, the mHUD was evaluated in a ship bridge simulator by 12 navigation students. Results revealed that the mHUD substantially improved situational awareness, proving more efficient and effective than navigating without it in poor visibility conditions. Participants found the mHUD easy to learn and expressed willingness to use it in real-world situations. The study highlights the mHUD's potential to enhance situational awareness on ship bridges and ROCs for autonomous ships, while suggesting potential enhancements to increase usability and user satisfaction.
The article is devoted to the mathematical theories and algorithms necessary for the implementation of a software package that fully automates the calculations necessary in Nautical Astronomy. The article describes a method for calculating the equatorial and horizontal coordinates of the celestial bodies at any moment of time. The authors describe the calculation of the time of the apparent rising (setting) of the Sun, solar illumination and events of other celestial bodies. A formula for calculating astronomical refraction is proposed. A matrix method for implementing the method of least squares for determining the coordinates of a place along the lines of position is described. An algorithm for identifying navigational planets is also described and a method for estimating the error for it is proposed. Based on this, the results of the development of the software package 'Astronomy Package' for Nautical Astronomy are presented.
