Ship Navigation Research Articles

Enhancing the reliability of shipborne INS/GNSS integrated navigation system during abnormal sampling periods using Bi-LSTM and robust CKF

Developing a reliable navigation system is a crucial challenge for intelligent ships. Integrating Inertial Navigation Systems (INS) with shipborne Global Navigation Satellite Systems (GNSS) provides a viable solution. However, the system's reliability heavily relies on GNSS availability. This paper proposes a method to enhance the reliability of shipborne INS/GNSS integrated navigation systems during GNSS abnormal sampling periods. The proposed method consists of two parts. Firstly, using specially designed input and output parameters, a pseudo-GNSS prediction model based on a Bidirectional Long Short-Term Memory Recurrent Neural Network (Bi-LSTM) is constructed to generate predicted pseudo-GNSS measurements during periods of GNSS loss, effectively filling in the gaps left by the absence of actual GNSS data. Secondly, Huber's M-estimation-based Robust Cubature Kalman filter (RCKF) is integrated into the data fusion algorithm, and a switching mechanism between RCKF and the Cubature Kalman filter (CKF) is established based on the source of GNSS data utilized for measurement update, to mitigate the impact of non-Gaussian distribution prediction noise introduced by pseudo-GNSS prediction model. Experimental validation using real ship navigation data in a natural marine environment confirms the proposed method's capability to maintain navigation accuracy during GNSS loss for shipborne INS/GNSS integrated navigation systems. The algorithm's superiority becomes more evident as the duration of GNSS loss increases, underscoring its effectiveness in enhancing shipborne integrated navigation system reliability.

A fast, high-precision deep learning model for regional wave prediction

Accurate prediction of regional significant wave height is crucial for safe ship navigation, route planning, and reducing carbon emissions from shipping. Physics-based wave prediction methods involve complex calculations, limiting real-time information provision. Convolutional Neural Networks (CNNs) have gained popularity for wave prediction but suffer from feature loss, positional insensitivity, and poor performance in high sea conditions. This study introduces a Vision Transformer-based regional wave prediction model (VIT-RWP) to address these issues. VIT-RWP utilizes an attention mechanism to extract the wind-wave mapping relationship. It employs convolution and transpose convolution as encoder and decoder, preserving positional information and relative point positions within the region. Evaluation in four sea areas, including comparison with CNN-RWP, demonstrates VIT-RWP's advantages. Pre-training enhances VIT-RWP's predictive accuracy by over 0.5%, surpassing CNN-RWP by over 5%. VIT-RWP maintains accuracy even with wave heights exceeding 5 m. Importantly, it exhibits remarkable robustness when subjected to Gaussian noise in input data. VIT-RWP's consistent performance across diverse seas establishes its efficacy and accuracy as a reliable wave prediction model.

Risk analysis of bridge ship collision based on AIS data model and nonlinear finite element

Abstract To solve the problem of calculating the probability of ship collision in ship bridge collision risk assessment, the impact parameters of ship collision are obtained based on the automatic identification system (AIS) data to solve the problem that the existing methods do not consider the actual navigable ship information in a specific bridge navigation area. Based on AIS data, the dynamics of sailing ships are analyzed, parameters such as ship position, speed, and yaw angle, are obtained, ship traffic flow is analyzed, and the geometric probability in the actual ship traffic flow specification model is modified. The results before and after correction were compared and analyzed. The results show that the maximum transverse displacement of the anti-collision device is about 1.5 s under all working conditions, indicating that the collision force drops continuously from this moment to 0 in the collision force time history. In the process of collision, the anti-collision device absorbs part of the collision energy through its own deformation. Under the premise of a certain initial kinetic energy, the deformation caused by the collision energy absorption after fortification will be reduced. The anti-collision device has local permanent deformation under the impact of 5,000 t ship, but no damage and failure, and will not cause water entry and subsidence. It proves that the constructed fishery ship collision risk assessment model and the developed fishery ship safety management and evaluation system are reliable and the prediction results are credible, which can provide scientific methods for the safety management and evaluation of fishery vessels. For the bridge area with complicated ship navigation conditions, it is necessary to use the actual navigable ship information obtained based on AIS data to estimate the distribution of ships in the bridge area to improve the accuracy of the calculation results.

An Intelligent Monitoring System for the Force Characteristics of Floating Bollards in a Ship Lock

Due to the large scale of navigation ships, the fast speed of entering the lock, and the irregular mooring and the complicated flow conditions in the lock chamber, it is common for the floating bollards of the lock to suffer structural damage or even failure due to the overloaded mooring force. However, the traditional cable load measurement method cannot offer real-time feedback on force characteristics of floating bollards, making it difficult to accurately judge its service status. To this end, according to the floating bollard structure type and load condition of a representative ship lock project in China, this paper determines the theoretical model parameters of a floating bollard load response based on three-dimensional finite element numerical simulation test data and constructs a modified load response model of floating bollards. On this basis, an intelligent floating bollard monitoring system based on big data, internet, and cloud services is developed to intelligently perceive real-time floating bollard force characteristics and monitor the long-term service status. Relying on a representative ship lock in China, a field test of the floating bollard intelligent monitoring system is carried out. The relative error between the calculated values via the model (i.e., system exhibition results) based on the numerical results and the field-measured values is within 15%. This result verified the accuracy and effect of the monitoring system. This research supports the establishment of the digital perception monitoring platform for ship lock facilities and improves the automation level of ship lock operation and management as well as overall risk prevention and control capabilities.

An improvement in accuracy and spatial resolution of the pattern-matching sea ice drift from SAR imagery

ABSTRACTSea ice drift is a crucial parameter for sea ice flux, atmospheric and ocean circulation, and ship navigation. Pattern matching is widely used to retrieve sea ice drift from Synthetic Aperture Radar (SAR) data, but it often yields mismatched vectors and coarse spatial resolution. This study presents a framework to enhance the spatial resolution and accuracy of pattern-matching sea ice drift derived from SAR images. The framework employs the Accelerated-KAZE feature extraction method and Brute-Force feature matcher to extract feature-tracking sea ice drift vectors from SAR data, with mismatched vectors subsequently removed. The pattern-matching vectors are then refined by fusing with these feature-tracking vectors, using a Co-Kriging algorithm. Using the sea ice drift product from the Technical University of Denmark space as the pattern-matching vector field for refinement, the framework's effectiveness is evaluated by comparing the refined vectors with buoy displacements and pattern-matching vectors across five selected regions. Results show a reduction in velocity and direction root mean square error (RMSE) by 0.47 km/d (22%) and 4.97° (28%), respectively, and an enhanced spatial resolution from 10 km to 1 km. The findings demonstrate the framework's success in improving the accuracy and resolution of pattern-matching sea ice drift from SAR imagery.

Study on the influence of shaft brackets on the scale effect of the four-screw ship wake field

In the four-screw ship navigation, the load difference between the inner and outer propeller is found to be more than twice of that in the model test, indicating a serious scale effect. The stern attachment of the four-screw ship has a significant impact on the inner and outer propeller wake which directly affecting the load distribution. Thus, to investigate the impact of the brackets on the scale effect of the four-screw ship flow field, this paper conducted multi-scale simulations of a four-screw ship with and without bracket using the Reynolds-averaged Navier-Stokes (RANS) method. The feasibility and accuracy of the numerical method was verified by comparing with PIV test results. The result shows that the scale effect is more pronounced in the outer propeller compared to the inner propeller. The presence of bracket reduces the wake fraction difference between the inner and outer propellers, but significantly affects the inner radius region of the outer propeller disk, more than doubling the wake fraction in this region at full-scale. When the bracket Reynolds number falls below its critical Reynolds number, the inner radius region of the outer propeller flow field deviates notably from the full-scale results and loses its practical significance.

Prediction of ship trajectory based on deep learning

The rapid development of computer technology strongly promotes the study of maritime traffic safety. The application of artificial intelligence technology makes the ship’s trajectory prediction not limited to complex physical models, and improves the generality of ship’s track prediction. To address the problems of relatively poor prediction accuracy in existing ship trajectory prediction research and high coupling of algorithms during ship navigation while the prediction model needs to be improved, this paper combines grey forecast prediction model and long and short-term memory (LSTM) neural network to establish a neural network model for ship trajectory prediction. This model can effectively increase the number of messages in LSTM model, and reduce the loss value of the model and improve the accuracy of ship track prediction. The prediction method in this paper is verified by AIS data of a ship. The results show that compared with other time series prediction algorithms, this algorithm has higher prediction accuracy.

A POINT OF VIEW ON FORMATIVE- EVALUATION OF PART- TIME STUDENTS IN CONSTANTA MARITIME UNIVERSITY- A CASE OF STUDY

Part- time education is very attractive for students and universities, due to its obvious benefits related with economic, mobility and friendly education environment aspects. In Constanta Maritime University, part- time education is available for marine engineering, electrical engineering and navigation and waterborne transport programs. According to the Romanian legislation, the evaluation of part- time students consists in two types of assessment: formative evaluation and final evaluation. The formative evaluation is carried on by the use of the university’ E- Platform, while the final evaluation is face to face type- exactly like the one of full- time students. In the case of study, it is described the formative evaluation of part- time students enrolled in Marine Engineering program, second year of study, in the case of students’ performance assessment in Thermodynamics 1 Course. There are enrolled 52 students, but only 25 attended the formative evaluation, despite of the fact that this evaluation has a percentage in the final grade. This paper describes the manner in which this evaluation took place. Also, where analysed the reasons for not attendance and mistakes in test solving. Where identified the ways in which students performance might be improved, for a better understanding of the curricula.

Multi-functional and Practical Adaptive Collision Avoidance Decision-making System for Autonomous Ships

In the current landscape, autonomous navigation systems face challenges due to incomplete functionality and low integration. They also lack a comprehensive, real-time, and accurate navigation risk assessment, while operating independently from one another. To address these issues, a multi-functional and practical adaptive collision avoidance decision-making system for autonomous ships is developed in this paper. The system primarily relies on the electronic navigational chart (ENC) to effectively display the ship’s navigation situation. It establishes a collision avoidance decision-making model centered around the own ship, offering collision risk analysis, avoidance methods, and optimal timing to ensure safe navigation. The system efficiently integrates and processes multimodal maritime data from various devices, utilizing ontology-based approaches for comprehensive navigation situation understanding, which integration provides invaluable support for intelligent decision-making processes. With a user-friendly interface, excellent portability, and cross-platform interoperability, this system has undergone collaborative efforts and joint debugging with China Shipbuilding Navigation Technology Co., Ltd. during the project’s first and second phases. Notably, the system has been successfully implemented on the “High-performance Integrated Bridge System” platform of China Shipbuilding Navigation, showcasing its potential for advancement from principle prototype development to actual equipment application. By offering accurate collision avoidance decision support, this system significantly contributes to enhancing the safety of ship navigation.

Multiple faults diagnosis for ocean-going marine diesel engines based on different neural network algorithms.

Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely involved. Due to the strong correlation, non-linearity and randomness of multiple faults, it is extremely difficult to make an accurate diagnosis. In this study, diagnosis methods based on thermal parametric analysis combined with different neural network algorithms were established and used for the diagnosis of multiple faults in the ocean-going marine diesel engine. The results show that the Levenberg Marquardt back propagation neural network has the highest diagnostic accuracy rate of 88.89% and 100% for multiple faults and single faults, respectively, and its diagnostic time is also relatively short, 0.78 s. The Bayesian regularization back propagation neural network can give a diagnostic accuracy rate of 100% for single faults, but for multiple faults, the diagnostic accuracy rate is only 55.56%, and the diagnosis time for the entire sample is the longest. As for the probabilistic neural network, although it has the fastest diagnosis speed, it has the lowest diagnostic accuracy rate for both single faults and multiple faults. The results may provide references for the online diagnosis of single faults and multiple faults in ocean-going marine diesel engines.

The importance of communication skills in the formation of leadership competence of future specialists in navigation and management of ships

The article analyses the content of leadership competence and substantiates the need for communication skills for the professional activity of future specialists in ship navigation and ship handling. It is determined that the formation of leadership competence is aimed not only at achieving personal success, but also at ensuring the safety of crew members and strengthening the mental health and well-being of seafarers. The study has shown that the priorities in the development of leadership competence are communication skills (diplomacy, developed oratory skills, persuasiveness); determination (stress resistance); responsibility and dedication; development of thinking (speed, logic, criticality). The necessity of improving the «soft» skills of seafarers is actualised, which is possible through the introduction of the author's special course and active forms of work in the educational process of higher education institutions, aimed at developing leadership competence in specialists in ship navigation and ship management. It is substantiated that the leader's communication skills can be useful for the following purposes: improving crew morale, increasing the productivity of professional duties and facilitating internal communication. It is established that the formation of leadership competence involves taking into account individual psychological characteristics in the process of formation, high self-esteem, positive attitude, cognitive activity, emotional stability, courage, self-control, communication and organisational skills and systematic creation of situations. The prospects for the development of communication skills are to improve the maritime education system at all levels and to provide conditions for the development of leadership competence of future officers

“The Last Victims of the Indian War”: Celilo Falls, the Dalles Dam, and Infrastructural Colonization

In 1957, the Dalles Dam was constructed on the Columbia River between Washington and Oregon. When the dam was completed, it inundated Celilo Falls, a Native American fishery and cultural gathering point that had been in use for at least 12,000 years. Prior to dam construction, the federal government and local agencies issued a number of reports stating the necessity of the dam for economic development through hydroelectric power generation, improved shipping navigation, flood control, and expanded irrigation capacity. These reports often sought to determine the financial payout that would be made to the groups with treaty rights to fish at their “usual and accustomed” places, such as Celilo Falls. However, the reports rarely engage with the cultural significance of Celilo Falls or the depth of opposition that people had to the dam. The research discussed here is based on archival government reports alongside the voices of affected tribal members preserved through The Confluence Project and other sources. This article develops the idea that reclamation infrastructure in the U.S. West plays a key role in colonizing efforts from federal to local scales, reflecting the aims of the settler state. Through an analysis of government documents alongside the recollections of Indigenous elders from the mid-Columbia region, this article offers insights into how reclamation infrastructure functions as an aspect of settler colonialism and relies on theorizations of this process from the people most affected by the loss of Celilo Falls.

Neuroscience Approach to Situational Awareness: A Research on Marine Navigation

Neuroscience Approach to Situational Awareness: A Research on Marine Navigation

A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions

A mathematical model is created to obtain safe navigation for ships in regular head waves in this study. To validate the suggested model, firstly, the added resistances are calculated for two different ships using empirical formulas in the mathematical model. Secondly, the turning test simulations are performed for calm water and in waves with various wave amplitudes. After these validation studies, the path following simulation of the ship to the target destinations is performed in both waves and calm water for the determined course. It is assumed that regular head waves affect the ship as an external disturbance. The wavelengths and wave amplitudes are changed systematically to understand their effect during the path following simulations. When the ratio of wavelength to ship length, λ/Lpp, is nearly 1.0, the path following simulation times increase. Moreover, when the value of wave amplitude increases, so does the simulation time.

POLITICAL AND LEGAL FACTORS OF THE FUNCTIONING OF THE HUMANITARIAN CORRIDOR IN THE BLACK SEA

The article analyzes the prerequisites and factors of the functioning of the humanitarian maritime corridor in the Black Sea. New Ukrainian realities caused by Russia’s large-scale armed aggression against Ukraine. The blockade of seaports and ships caused the export of Ukrainian agricultural products and other goods from all ports of Ukraine to stop. The impossibility of exporting grain from Ukraine first of all had a painful impact on world food security. Therefore, the world community and the Ukrainian authorities faced the problem of unblocking sea routes and returning Ukrainian grain crops to foreign markets. Solving this problem required the development of economic, legal, political, and logistical components, taking into account the interests of the participating countries and other countries of the world. It became possible to settle the issue only within the framework of the «Initiative on the Safe Transportation of Grain and Foodstuffs from Ukrainian Ports», which, under the norms of International Maritime Law, established mechanisms for safe maritime commercial transportation, and allowed the creation of a humanitarian («grain») corridor in the Black Sea. The achievement of the «grain corridor» was the stabilization of the food market and the maintenance of food security. At the same time, Russia also received tactical, strategic, economic, and political benefits. Despite this, the Russian Federation continues to obstruct freedom of navigation in the Black Sea and the Danube for political and military purposes to dominate the Black Sea region. In this regard, solving the problem of the effective functioning of the humanitarian corridor in the Black Sea is complex and requires a multilateral approach, which involves a reliable legal and regulatory framework, transparency, and international cooperation to develop political and economic mechanisms to counter Russian aggression and ensure free sea navigation for commercial ships.

A multi-task deep learning model integrating ship trajectory and collision risk prediction

Trajectory prediction can provide position information for ship navigation behavior perception. This is very important for intelligent navigation. However, potential collision risk caused by other ships’ navigation behaviors will directly affect the navigation behavior of own ship. In order to incorporate the potential collision risk caused by the navigation behavior of other ships, this study proposes a Multi-Task Deep Learning Model to exploit the interaction between ships and predict the ship trajectory and potential collision risk simultaneously. Trajectory prediction task can exploit the knowledge contained in collision risk prediction task and the prediction of collision risk can utilize the navigational situation in the future. In the trajectory prediction task, we construct virtual channel center points in a data-driven way to capture the ship behavior in ship traffic flow and use them as constraints in our designed loss function. Furthermore, we design the soft collision risk classification label and the knowledge distillation module to improve the accuracy of the multi-task model. At last, we demonstrate that our method can not only improve the accuracy of trajectory prediction, but also gain reliable potential collision risk results. Our method improves the prediction time scale by an order of magnitude compared with other methods.

TTMRN: A topological-geometric two-layer maritime route network modeling for ship intelligent navigation

The construction of maritime route networks holds significant importance for autonomous navigation of vessels. In this study, a two-layer maritime route network modeling method based on huge amounts of ship trajectory data was proposed. Firstly, we introduce a novel method for extracting nodes of the marine route network, which identifies feature points in ship trajectories through clustering. Secondly, we use a spatial computing method to transform ship trajectory data into a sequence of waypoint regions and establish a node connection matrix to realize the nodes' connection of the topological layer route network. And routes are extracted between waypoint regions to characterize the connection relationship of the geometric layer network. Finally, by connecting nodes of the topological layer with the support of the connection matrix and waypoint regions of the geometric layer with the route, the two-layer maritime route network that combines topological and geometric layers is constructed. The proposed method was applied to the waters of Vancouver, successfully constructing a topological-geometric two-layer maritime route network. Overall, the proposed method is beneficial for improving the safety and efficiency of autonomous navigation of ships, and has a positive impact on the development of smart shipping industry.

A Dynamic Bayesian Network Probability Framework for Typhoon Risk Analysis in the Land-sea Regions

The life cycle of a typhoon is mainly divided into three stages: far away from the coast, offshore, and on land. The disaster bodies and typhoons affected are different at every stage. The traditional assessment methods could not consider the risk from the complete typhoon life cycle to cover the effects of both land and sea. This paper proposes a Bayesian network risk assessment model to evaluate the navigational ship risk and damage risk of coastal structures during the different typhoon life cycles. The disaster memory database of the typhoon is also constructed in this paper, which deconstructs the typhoon risk factors and forms a unified description semantics.

Research on Multi-Sensor Data Fusion Positioning Method of Unmanned Ships Based on Threshold- and Hierarchical-Capacity Particle Filter

To improve the positioning accuracy of unmanned ships, a multi-sensor system including ZigBee, a Global Positioning System (GPS), and BeiDou Navigation Satellite System (BDS) is constructed, and an adaptive multi-sensor data fusion positioning method based on the threshold and hierarchical capacity particle filter (TCPF) is designed. First, the ZigBee-GPS/BDS multi-sensor measurement data is preprocessed to achieve a consistent space–time reference and transformed into the same coordinate system by projection. Then, the fault data is weighted and corrected through the consistency inspection of ZigBee-GPS/BDS multi-sensor positioning data, and the corresponding confidence factor is given according to the confidence distance of the positioning data; furthermore, the confidence factor is associated with stratified sampling. After that, the multi-sensor positioning data is filtered and denoised using a basic particle filter. Finally, a TCPF data fusion algorithm is designed, and the navigation positioning data of the unmanned ship is fused and filtered to obtain its positioning information. Numerical tests show that compared with other filtering algorithms, the mean square root error and standard deviation of the proposed TCPF algorithm decrease by an average of 25.0% and 28.0%, respectively, which verifies its high filtering accuracy and its advantages in suppressing particle degradation and avoiding sample scarcity. The experimental tests show that compared with other fusion algorithms, the proposed TCPF algorithm can not only realize the precise positioning during unmanned ship navigation, but also in the positioning and fault tolerance test, the average positioning error, root-mean-square error, and standard deviation of the former decrease by 36.0%, 38.0%, and 37.0%, respectively, and the corresponding performance indicators of the latter decrease by an average of 20.0%, 19.5%, and 17.5%, which verifies that it has the advantages of high data reliability and good filtering fault tolerance, and helps to improve the positioning accuracy of unmanned ships.

The Design Discussion of a Self-Powered Automatic Identification System Sensor

In recent years, many vessels have utilized Automatic Identification Systems (AIS) to process marine positioning and navigation; the system employs GPS to position and transmits signals through ultra-high frequency wireless communication, which can deliver the signals to a broader range. However, due to the high power-consumption character of AIS, it is necessary to consider an appropriate power supply to support the system. In other words, AIS is suitable for various application fields, but the power supply issue remains unresolved in different Internet of Things (IoT) equipment, such as marine movement or land activity positioning. This article proposes a sensor designed with a self-powered AIS, and the primary functions are: 1. Available for customizing the signal sending time intervals based on the site needs; 2. A panic button is designed in the system for activating AIS signal transmission; 3. The equipment stores electricity in solar panels to improve the power consumption issue of AIS; 4. The g-sensor installed in the equipment detects and judges AIS motion and wobble to position the vessel. The AIS equipment presented in this research mainly applies to positioning marine and mountain activities or ocean farming. Due to the lack of power supply in the applications mentioned above, if AIS cannot deliver signals for a long time, the equipment will influence the power issue of other detectors in the entire design. Therefore, this study discusses a design combined with a self-powered AIS detector, allowing the AIS equipment to operate for a year and be suitable for various applications. This design can resolve the lack of mobile communication issues on the sea or in the mountain.