Research into statistical distributions of φ, λ and two-dimensional (2D) position errors of the global positioning system (GPS) enables the evaluation of its accuracy. Based on this, the navigation applications in which the positioning system can be used are determined. However, studies of GPS accuracy indicate that the empirical φ and λ errors deviate from the typical normal distribution, significantly affecting the statistical distribution of 2D position errors. Therefore, determining the actual statistical distributions of position errors (1D and 2D) is decisive for the precision of calculating the actual accuracy of the GPS system. In this paper, based on two measurement sessions (900,000 and 237,000 fixes), the distributions of GPS position error statistics in both 1D and 2D space are analysed. Statistical distribution measures are determined using statistical tests, the hypothesis on the normal distribution of φ and λ errors is verified, and the consistency of GPS position errors with commonly used statistical distributions is assessed together with finding the best fit. Research has shown that φ and λ errors for the GPS system are normally distributed. It is proven that φ and λ errors are more concentrated around the central value than in a typical normal distribution (positive kurtosis) with a low value of asymmetry. Moreover, φ errors are clearly more concentrated than λ errors. This results in larger standard deviation values for φ errors than λ errors. The differences in both values were 25–39%. Regarding the 2D position error, it should be noted that the value of twice the distance root mean square (2DRMS) is about 10–14% greater than the value of R95. In addition, studies show that statistical distributions such as beta, gamma, lognormal and Weibull are the best fit for 2D position errors in the GPS system.

Vehicle state estimation and path prediction, which usually involve Kalman filter and motion model, are critical tasks for intelligent driving. In vehicle state estimation, the comparative performance assessment, regarding accuracy and efficiency, of the unscented Kalman filter (UKF) and the extended Kalman filter (EKF) is rarely discussed. This paper is devoted to empirically evaluating the performance of UKF and EKF incorporating different motion models and investigating the models’ properties and the affecting factors in path prediction. Extensive real world experiments have been carried out and the results show that EKF and UKF have roughly identical accuracy in state estimation; however, EKF is faster than UKF generally; the fastest filter is about 2⋅6 times faster than the slowest. The path prediction experiments reveal that the velocity estimate and the used motion model affect path prediction; the more realistically the model reflects the vehicle's driving status, the more reliable its predictions.

Seafaring, as a traditionally male-dominated industry, continues to have very few female seafarers, with approximately 2% globally being women. This paper draws on the findings of a study that considered both the experiences of women seafarers working in the UK shipping industry and the views of key industry stakeholder representatives, and asks what must be done to improve those experiences? Responses across the industry suggest that all women seafarers will experience some form of harassment during their careers, which has significant implications for their occupational health, safety and wellbeing. These experiences reflect failures of leadership in developing and promoting a safe and inclusive onboard culture. This paper calls for fundamental change within the industry, including improvements in training and leadership to reflect modern seafaring and diversity on board. It also calls for relevant policy and strategic changes to be based on the views of seafarers and their representatives. It concludes that improving the experiences of women on board will improve the occupational health, safety and wellbeing of all seafarers, regardless of gender or any other characteristic or classification.

Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.

Non-compliance with or misinterpretation of the International Regulations for Preventing Collisions at Sea (COLREGs) when assessing vessel encounters, and the lack of good communication between the vessels involved in a critical situation, are primary contributing factors in collisions. Vessels engaged in an encounter should be aware that they are part of the same scenario and situation, which can become critical. Sharing and contrasting their information about the encounter would help those responsible to take manoeuvring decisions in a consistent way. There are situations whose evaluation by the respective officers in charge of the navigational watch may diverge and lead to disagreements on the actions to be taken. If there is no proper inter-ship communication, a collision may result. This paper presents a proposal for safety communication implemented in a programmable system using common equipment (automatic identification system), and applies it to a case study of one such special situation, showing how it could help to reduce the risk of collision.

The layout of seafloor datum points is the key to constructing the seafloor geodetic datum network, and a reliable underwater positioning model is the prerequisite for achieving precise deployment of the datum points. The traditional average sound speed positioning model is generally adopted in underwater positioning due to its simple and efficient algorithm, but it is sensitive to incident angle related errors, which lead to unreliable positioning results. Based on the relationship between incident angle and sound speed, the sound speed function model considering the incident angle has been established. Results show that the accuracy of positioning is easily affected by errors related to the incident angle; the new average sound speed correction model based on the incident angle proposed in this paper is used to significantly improve the underwater positioning accuracy.

The objective of this work was to assess whether the implementation of a bridge resource management (BRM) unit into the simulator-based nautical training of the German Navy is effective in improving non-technical skills and navigation performance. To this end, questionnaire data, observations of behaviour and performance outcomes were compared between a control group and an experimental group. Data of 24 bridge teams (126 sailors) were used for the analyses. Ten teams received BRM training and 14 teams served as the control group with unchanged simulator training. Reactions to simulator training were positive in both groups but more favourable in the control group. In the BRM group, significantly more positive attitudes towards open communication and coordination, more frequent sharing of information and fewer collisions were found than in the control group. Effect sizes were rather small. This may be due to the limited scale of the BRM unit, which consisted of only one instruction-training-feedback cycle. The extension of BRM-related feedback to all simulator runs of the nautical training can be expected to produce larger effects on attitudes, behaviour and performance.

This study investigates the use of augmented reality technology (AR) in the field of maritime navigation and how researchers and designers have addressed AR data visualisation. The paper presents a systematic review analysing the publication type, the AR device, which information elements are visualised and how, the validation method and technological readiness. Eleven AR maritime solutions identified from scientific papers are studied and discussed in relation to previous navigation tools. It is found that primitive information such as course, compass degrees, boat speed and geographic coordinates continue to be fundamental information to be represented even with AR maritime solutions.

The spatial distribution of collaborative targets and the information collaboration process are two important factors affecting the efficiency of real-time collaborative navigation. Addressing these factors, this paper presents the following work. First, the collaborative communication process between navigation targets is designed and illustrated with an application example. Second, the feature and error condition of the spatial distribution of collaborative targets is analysed. Then, a method based on CGDOP (collaborative geometric dilution of precision) value is proposed for the evaluation of the actual spatial distribution conditions of collaborative targets. Finally, a simulated experiment is conducted to evaluate the collaborative navigation process and the collaboration effect of the collaborative navigation network in different spatial shapes. Overall, the results of this study optimised the observation and application efficiency of navigation data, and improved the stability and reliability of real-time navigation service through multi-target collaborative navigation.

With the characteristics of full autonomy and no accumulated errors, polarisation navigation shows tremendous prospects in underwater scenarios. In this paper, inspired by the polarisation vision of aquatic organisms, a novel point-source polarisation sensor with high spectral adaptability (400 nm–760 nm) is designed for underwater orientation. To enhance the environmental applicability of the underwater polarisation sensor, a novel sensor model based on the underwater light intensity attenuation coefficient and optical coupling coefficient is established. In addition, concerned with the influence of light intensity uncertainty on sensor performance underwater, an antagonistic polarisation algorithm is adopted for the first time, to improve the accuracy of angle of polarisation and degree of polarisation in the low signal-to-noise ratio environment underwater. Finally, indoor and outdoor experiments are carried out to evaluate the performance of the designed polarisation sensor. The results show that the designed point-source polarisation sensor can acquire polarised light and be used for heading determination underwater.

Applying the existing domain model in ship domain research in a restricted area can be difficult owing to multiple factors that must be considered. This study presents a new domain model that can be applied in such environments. According to Endsley's theory of situation awareness, people have their own criteria in decision making based on factors such as individual and environmental factors. To investigate these factors, 125 seafarers were interviewed and threshold values based on their awareness were examined. The factors were evaluated as the closest points of approach. Domain modelling was performed based on these seafarer awareness values; then, the existing and awareness domains were compared, revealing that the awareness-based domain of seafarers is more affected by ship manoeuvrability than by ship size and speed, unlike the findings of existing domain research. Therefore, this study presents a new domain model that is both realistic and practical in a confined area, including various factors considered by seafarers based on the awareness values formed.

Radio navigation systems (RNSs) are commonly applied in air, land and marine navigation. They are most often used for position determination. However, when comparing the definitions of this concept provided by global organisations, such as the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA), International Civil Aviation Organization (ICAO), International Maritime Organization (IMO) or International Telecommunication Union (ITU), it should be noted that the definitions presented differ significantly. Due to the ambiguity and numerous contradictions, the RNS classification varies depending on the definition of the term ‘radio navigation’ adopted, which poses serious interpretation problems. This article analyses the concept of radio navigation based on the most important global documents and legal acts on this issue. It points to fundamental differences in the understanding of the term and proposes the adoption of a new, uniform definition for air, land and marine navigation. Based on this definition, the current paper proposes a uniform RNS classification. The proposed definition of the concept of radio navigation and the resulting RNS classification are essential for the understanding of this term to achieve global uniformity and fundamental to the harmonious development of the scientific discipline of navigation. The current proposal should initiate a discussion on the meaning of the concept of radio navigation and the RNS classification.

Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.