This paper proposes a novel method of applying an iterative generation differential equation method to the multi-component nonlinear signal analysis of a diesel engine. The characteristics of a dynamic model of the single cylinder are analysed and discussed. The iterative generation differential decomposition method decomposes the multi-component signal and extracts multiple single-component signals. The sensitive single-component analysis technology of the complex vibration signal of a diesel engine is formed. The relationship between characteristic parameters of engine vibration dynamics and operation law is derived. A priori information about the unmeasured vibration signals of the roll-on/roll-off (Ro-Ro) passenger ships is not required. The experimental data is validly processed based on this developed method. Results show that this method is practical and feasible in analysing diesel engine vibration signals, especially under different load operating conditions.

This study describes an optimal method for deploying rescue ships in response to marine accidents using dynamic programming and particle swarm optimisation in an archipelago. We solved the shortest distance problem from a rescue ship to a marine accident using dynamic programming, which avoids obstacles, such as land or aquacultures. The optimal location problem is NP-hard. However, the optimal locations were found to be efficient among the various candidate combinations using particle swarm optimisation. We compared two models based on the set covering location model (SCLM) and P-median model (PMM). The PMM outperformed the SCLM approach in the test. The findings of this study may be valuable for directing judgments regarding search and rescue (SAR) vessel placements to maximise resource utilisation efficiency and service quality. Furthermore, this process can flexibly arrange multiple rescue ships.

The recently discovered social place cells and grid cells in hippocampal formation are believed to be the neural basis underlying relative navigation of conspecifics. In this paper, we propose a new brain-inspired relative navigation model in a large-scale 3D environment for collective UAVs that translates the neurodynamics of the social place cell–grid cell circuit to robotic relative navigation algorithm for the first time. Our approach comprises three key parts: (1) a 3D isotropic Gaussian function-based cube social place cell network (cube-SPCNet), (2) a 3D continuous attractor neural network-based cube grid cell network (cube-GCNet), and (3) a population vector-based neural decoding module. The resulting brain-inspired relative navigation model incorporates the good relative information abstraction capabilities of the cube-SPCNet with the powerful temporal filtering capabilities of the cube-GCNet, yielding robustness and accuracy performance improvement for relative navigation. Experimental results show the new method can provide more robust and precise relative navigation results than its conventional counterpart, displaying a possible brain-inspired solution for relative navigation enhancement for collective UAVs.

Traditional radiometric tracking navigation increasingly fails to meet the demands of deep space exploration. In contrast, optical navigation enables interplanetary spacecraft to navigate autonomously with higher precision. The effectiveness of image processing algorithms plays a crucial role in determining the accuracy of optical navigation systems. This paper presents a robust centroid extraction method based on a hybrid genetic algorithm. First, noise interference is effectively reduced by leveraging proximity information. Second, a fitness evaluation mechanism is introduced to assess model performance throughout the iterative process. Third, an annealing mutation operator is incorporated to prevent premature convergence to local optima. Finally, extensive comparative testing demonstrates that the proposed method offers substantial improvements in both accuracy and robustness, thereby substantially improving the reliability of the navigation system under complex conditions.

Research findings based on the data of current automatic identification systems (AISs) can only be applied to some parts of navigation research owing to their insufficient mining depth. Previously, route planning research has been based on the waypoint and corresponding optimised algorithm without considering the actual navigation situation and sailing habits. The planned route considerably differs from the actual sailing route, and the application result is undesirable. A novel solution to support the route planning problem has been introduced owing to the large accumulation of AIS big data. In this study, the ship navigable route framework (SNRF) which is reflected by real data via mining AIS big data serves as the basic network for the planned maritime route. This study uses the concept of manifold distance based on AIS big data to build a maritime SNRF through high-density searching. It can provide basic theoretical support for actual navigation distance calculation, route planning and route accessibility inspection in the future.

Identifying the absence of situation awareness (SA) in air traffic controllers is critical since it directly affects their hazard perception. This study aims to introduce and validate a multimodal methodology employing electroencephalogram (EEG) and eye-tracking to investigate SA variation within specific air traffic control contexts. Data from 28 participants executing the experiment involving three different SA-probe tests illustrated the conceptual relationship between EEG and eye-tracking indicators and SA variations, using behavioural data as a proxy. The results indicated that both EEG and eye-tracking metrics correlated positively with the SA levels required, that is, the frequency spectrum in the β (13–30 Hz) and γ (30–50 Hz) bands, alongside the fixation/saccade-based indicators and pupil dilation increased in response to higher SA levels. This research has substantial implications for investigating SA using a human-centric approach via psychophysiological indicators, revealing the intrinsic interactions between the human capability envelope and SA, contributing to the development of a real-time monitoring system of SA variations for air transportation safety research.

Wall-climbing robots work on large steel components with magnets, which limits the use of wireless sensors and magnetometers. This study aims to propose a novel autonomous localisation method (RGBD-IMU-AL) with an inertial measurement unit and a fixed RGB-D camera to improve the localisation performance of wall-climbing robots. The method contains five modules: calibration, tracking, three-dimensional (3D) reconstruction, location and attitude estimation. The calibration module is used to obtain the initial attitude angle. The tracking and 3D reconstruction module are used jointly to obtain the rough position and normal vector of the robot chassis. For the location module, a normal vector projection method is established to screen out the top point on the robot shell. An extended Kalman filter (EKF) is used to estimate the heading angle in the attitude estimation module. Experimental results show that the positioning error is within 0⋅02 m, and the positioning performance is better than that of the MS3D method. The heading angle error remains within 3⋅1°. The obtained results prove its applicability for the autonomous localisation in low-texture and magnetically disturbed environments.

The International Regulations for the Prevention of Collisions at Sea (IRPCS) provide a comprehensive set of instructions for watchkeeping officers to follow and prevent collisions at sea. This study compares how six newly qualified deck officers and six Master Mariners, who were all trained at the same college, applied the IRPCS. Individual, semi-structured interviews were used to uncover how the 12 participants applied and interpreted the rules for three authentic scenarios. Phenomenography was used to capture the qualitatively different means by which participants interpreted the IRPCS. For basic collision avoidance situations, the results indicated little difference between the cohorts' ability to interpret and apply the IRPCS. However, when the scenarios became more complicated, Master Mariners outperformed newly qualified deck officers. In these cases, Master Mariners displayed a greater capacity to assess the overall situation, whereas newly qualified deck officers tended to simplify by focusing on a single rule. These findings indicate that training needs to focus on developing situational awareness; and training scenarios need to incorporate multiple vessels in authentic scenarios to enhance newly qualified deck officers' capacities to interpret the IRPCS.

Aiming at the error estimation problem of a radar detection system when the variation law of system error is unknown, an improved Gaussian mean-shift radar dynamic error registration algorithm (IGMSR) is proposed. The algorithm can effectively adapt to the variation of system error when the variation law of system error is unknown. The IGMSR algorithm uses the mean-shift method to contribute different characteristics to the estimation results of different sample points, and constructs weight coefficients according to the deviation of sample points from the mean and sampling time. The simulation results show that more than 90% of the constant system errors can be eliminated; for the systematic error with slow change, more than 80% of the bias can be eliminated in real time, while a previous method of Zhu and Wang (2018) can only eliminate 60% of the systematic error and require the change law to be known. This method overcomes the influence of random error and abnormal point, and the estimation results are more robust.

Safe and effective navigation of the world's oceans and waterways relies on maritime education and training. This involves the learning of motor, procedural and verbal components of complex skills. Motor learning theory evaluates training variables, such as instructions, feedback and scheduling, to determine best practices for long-term retention of such skills. Motor learning theory has come a long way from focusing primarily on underlying cognitive processes to now including individual and contextual characteristics in making predictions about instructional strategies and their role in performance and learning. A remaining challenge in applying recent motor learning theory to maritime education and training is a lack of empirical testing of complex vocational skills, such as simulation scenarios, with delayed retention and transfer tests. Incorporating theory-based understanding of beneficial instructional practices, through both cognitive approaches and those considering context and environment, task complexity and learner characteristics is a fruitful way forward in advancing maritime education and training.

In the contemporary maritime industry, characterised by intense competition, reduced visibility due to heavy fog is a primary cause of accidents, significantly impairing maritime operational efficiency. Consequently, investigating foggy weather navigation safety holds crucial practical significance. This paper, through an analysis and synthesis of various aspects of foggy navigation technology, including foggy navigation regulations at different ports, fog warnings, foggy vessel environmental perception and foggy auxiliary navigation systems, explores the key issues concerning vessel navigation during foggy conditions from a scientific perspective. This discussion encompasses the aspects of regulatory frameworks, standardisation, and the development of intelligent and responsive onboard equipment. Finally, the paper offers a glimpse into potential strategies for fog navigation.

Navigational safety is one of the important focuses of Maritime Education and Training (MET), and the quality of MET is the key to cultivating competent officers at sea. This study aims to understand better the effects of a rapid training method on ship handling and navigation in restricted waters, as well as decision-making skills under stressful situations. Tests were carried out in a simulator-based maritime training environment to explore the decision-making skills of maritime students in stressful situations under different training levels and methods. This study compares routine maritime training and task-aimed rapid training in improving manoeuvring and navigational and decision-making skills, and examines the training outcomes. The data used in this study is based on comparing the task performance and stress levels of the two groups of students using simulator-based training results from a designed scenario. The results analyse the training outcomes of decision-making skills and maritime operation performance by applying a specific decision-making model. In addition, the impact of students' stress levels was examined, both subjectively and objectively. The paper concludes with a set of recommendations for the design of future MET. The research helps enhance decision-making skills in maritime training programmes and understanding how learning in simulator-based maritime training environments can be improved.

To realise the overall calibration of the error model coefficients of accelerometers in an inertial combination and to improve the navigation accuracy of the inertial navigation system, a norm-observation method is applied to the calibration, especially for the quadratic coefficient of the accelerometer. The Taylor formula is used to expand the solution of the acceleration model, and the intermediate variables with error model coefficients are obtained using the least square method. The formulas for calculating the quadratic term coefficient, scale factor and bias of the accelerometer are given. A 20-position method is designed to calibrate the accelerometer combination, the effectiveness of the method is verified by simulation, and the effects of installation misalignment and rod-arm error on calibration accuracy are analysed. The results show that the installation misalignments and rod-arm errors have little influence on the coefficient calibration, less than 10−8, and can be neglected in a practical calibration process.

Information is provided to navigators through advanced onboard navigation equipment, such as the electronic chart display and information system (ECDIS), radar and the automatic identification system (AIS). However, maritime accidents still occur, especially in coastal and inland water where many navigational dangers exist. The recent artificial intelligence (AI) technology is actively applied in navigation fields, such as collision avoidance and ship detection. However, utilising the aids to navigation (AtoN) system requires more engagement and further exploration. The AtoN system provides critical navigation information by marking the navigation hazards, such as shallow water areas and wrecks, and visually marking narrow passageways. The prime function of the AtoN can be enhanced by applying AI technology, particularly deep learning technology. With the help of this technology, an algorithm could be constructed to detect AtoN in coastal and inland waters and utilise the detected AtoN to create a safety function to supplement watchkeepers using recent navigation equipment.

The development of Maritime Autonomous Surface Ship (MASS) is progressing rapidly within the maritime industry. Degree Two of MASS (MASS-DoA2), balancing human oversight and autonomous efficiency, will likely gain regulatory approval and industry acceptance. MASS-DoA2 possesses different control modes to adapt to various scenarios. However, the control-switching mechanisms among operators at shore control centres, autonomous navigation systems and number of seafarers onboard remain ambiguous, which poses a new risk that may significantly influence navigation safety. This study focuses on MASS-DoA2 and carries out a systematic review of autonomous ship guidelines. A questionnaire was designed based on the review findings, and a survey was carried out among captains and researchers in related fields. The review identified 11 control-switching scenarios with suggested takeover agents and the switching process and outlined the priority relationship between various takeover agents. Finally, a control-switching framework for MASS – DoA2 is proposed. It can serve as a theoretical framework for research on MASS's dynamic degree of autonomy and provide a reference for maritime regulatory authorities in establishing MASS – DoA2 control-switching mechanisms.

The identification of fishing vessel operations holds significant importance in addressing fishing industry issues, such as overfishing and illegal, unreported and unregulated fishing (IUUF). Many countries utilise data from vessel monitoring systems (VMSs) or automatic identification systems (AISs) to monitor fishing activities. These data include vessel trajectories, headings and speeds, among others. We aimed to analyse the fishing behaviours of three types of fishing gear used by vessels (trawl, purse seine and gill net) and identify the types of gear employed by the vessels. Therefore, a 1D CNN-LSTM fishing vessel operational behaviour prediction model was proposed by combining a one-dimensional convolutional (1D CNN) neural network and a long short-term memory (LSTM) neural network. The model utilises 1D CNN to extract local features from fishing vessel trajectories and employs LSTM to capture the time series information in the data, eventually classifying fishing gears. The results show that the proposed model achieves a classification accuracy of 92% in categorising fishing vessel operational trajectories. This study significantly contributes to preventing IUUF, curtailing overfishing, and enhancing fisheries management strategies.

In Global Navigation Satellite Systems (GNSS)-denied environments, aiding a vehicle's inertial navigation system (INS) is crucial to reducing the accumulated navigation drift caused by sensor errors (e.g. bias and noise). One potential solution is to use measurements of gravity as an aiding source. The measurements are matched to a geo-referenced map of Earth's gravity to estimate the vehicle's position. In this paper, we propose a novel formulation of the map matching problem using a hidden Markov model (HMM). Specifically, we treat the spatial cells of the map as the hidden states of the HMM and present a Viterbi style algorithm to estimate the most likely sequence of states, i.e. most likely sequence of vehicle positions, that results in the sequence of observed gravity measurements. Using a realistic gravity map, we demonstrate the accuracy of our Viterbi map matching algorithm in a navigation scenario and illustrate its robustness compared with existing methods.

Ship trajectory prediction plays a critical role in collision detection and risk assessment. To enhance prediction accuracy and efficiency, a novel hybrid particle swarm optimisation (PSO) and grey wolf optimisation (GWO) long, short-term memory (LSTM) network model is proposed (PGL model). The hybrid PSO-GWO optimisation method combines the algorithm's strengths and offers improved stability and performance. The hybrid algorithm is employed to optimise the hyperparameters of the LSTM neural networks to enhance prediction accuracy and efficiency. To demonstrate the superiority of the PGL model, the LSTM, PSO-LSTM and PGL are applied to the same dataset, and then prediction performance and processing time are compared. Experimental results indicate that the proposed PGL algorithm outperforms prediction accuracy and optimisation time.

The Automatic Identification System (AIS) is extensively used in monitoring vessel traffic, and ship navigation related information can be obtained from the AIS data. However, AIS data contain extensive redundant information, which leads to the general need to compress the data when applying it in practice or conducting research. In this paper, a three-dimensional compression of ship trajectories using the Dynamic Programming algorithm has been proposed. The AIS data near the ports of Long Beach and San Francisco in the United States were used to test and compare the Dynamic Programming algorithm with the Top-down Time-ratio algorithms. The experimental results show that the proposed algorithm can better retain the position and time information at low compression ratio such as 1%, 20% and 40%. Moreover, the algorithm is applicable to ship trajectories with different motion modes such as steering, mooring and straight ahead. The results show that the proposed algorithm can reasonably solve the problem of AIS data redundancy and ensure the quality of data, which is of practical significance for water transport, transport planning and other related research.

Vessel traffic services (VTS) is a marine information exchange system vital for the safety and efficiency of ship traffic within designated regions. The harmonisation, integration and exchange of marine information have emerged as significant components in promoting maritime safety, in line with the concept of e-navigation. This study aimed to analyse the flow of information between VTS areas employing social network analysis to ensure seamless marine information exchange across VTS areas. Information flow was analysed based on data obtained from ships navigating through coastal waters and ports in Korea, revealing that the sea area near Busan New Port exhibited the highest concentration of information flow, while the Tongyoung Coast VTS area represented a critical link in the flow of information. Given its history of marine accidents, the current Masan (Opko) VTS region emerged as a susceptible area. The study provides valuable foundational data for a comprehensive coastal surveillance system.