This research boarded on a novel initiative to replace the error-prone and labour-intensive process of converting Paper Nautical Chart (PNC) symbols to Electronic Navigational Chart (ENC) symbols with a more efficient and automated manner using Artificial Intelligence (AI). The proposed method applies the Convolutional Neural Network and YOLOv5 model to recognise and convert symbols from PNC into their corresponding ENC formats. The model's competence was evaluated with performance metrics including Precision, Recall, Average Precision, and mean Average Precision. Among the different variations of the YOLOv5 models tested, the YOLOv5m version revealed the best performance achieving a mean Average Precision of 0 ⋅ 837 for all features. A confusion matrix was used to visualise the model's classification accuracy for various chart symbols, underlining strengths and identifying areas for improvements. While the model has demonstrated high ability in identifying symbols like ‘Obstruction’ and ‘Major/Minor Lights’, it exhibited lesser accuracy with ‘Visible Wreck’ and ‘Background’ categories. Further, the developed graphical user interface (GUI) allowed users to interact with the artificial neural network model without demanding detailed knowledge of the underlying programming or model architecture.

We present a deep learning approach for near real-time detection of Global Navigation Satellite System (GNSS) radio frequency interference (RFI) based on a large amount of aircraft data collected onboard from the Global Positioning System (GPS) and Attitude and Heading Reference System (AHRS). Our approach enables detection of GNSS RFI in the absence of total GPS failure, i.e. while the receiver is still able to estimate a position, which means RFI sources with low power or at larger distance can be detected. We demonstrate how deep one-class classification can be used to detect GNSS RFI. Furthermore, thanks to a unique dataset from the Swiss Air Force and Swiss Air-Rescue (Rega), preprocessed by Swiss Air Navigation Services Ltd. (Skyguide), we demonstrate application of deep learning for GNSS RFI detection on real-world large scale aircraft data containing flight recordings impacted by real jamming. The approach we present is highly general and can be used as a foundation for solving various automated decision-making problems based on different types of Communications, Navigation and Surveillance (CNS) and Air Traffic Management (ATM) streaming data. The experimental results indicate that our system successfully detects GNSS RFI with 83$\,\cdot\,$5% accuracy. Extensive empirical studies demonstrate that the proposed method outperforms strong machine learning and rule-based baselines.

Reliable anti-collision control algorithms conforming with the rules regulating traffic at sea, the International Regulations for Preventing Collisions at Sea (COLREG), are essential for the deployment of autonomous vessels in waters shared with other ships. The development of such methods is an active field of research. However, little attention has been given to how these rules are interpreted by experienced mariners, and how such information can be parametrised for use in automatic control systems and autonomous ships. This paper presents a method for exploiting historical automatic identification system (AIS) data to characterise parameters indicating the prevalent practices at sea in encounters with high collision risk. The method has been tested on data gathered in areas off the Norwegian coast over several years. Statistics on relevant parameters from the resulting dataset and the relation between them is presented. The results indicate that the strongest influence on vessel behaviour is the type of situation, and the amount of land and grounding hazards in the vessel's proximity.

This paper presents the findings from a survey conducted in Norway to study the process of recovering from temporary disorientation in outdoor environments. The survey, with 693 respondents, investigated how individuals navigate and regain their bearings after getting disoriented for a short period of time. By collecting data on duration of disorientation and descriptions of participants' recovery experiences, we conducted both qualitative and quantitative analyses to establish a typology of spatial problem-solving [Downs, R. M. and Stea, D. (1977). Maps in Minds: Reflections on Cognitive Mapping. New York: Harper & Row, p. 55] approaches employed in wayfinding after becoming temporarily lost in outdoor settings. The research systematically explores the use of materials and approaches described by respondents when re-establishing their bearings. The existing research literature lacks comprehensive reporting on people's strategies for solving the problem of being disoriented in the outdoors, which motivated us to conduct this study. The resulting typology gives an overview of approaches employed to solve the problem of being lost in the outdoors and contribute additional details and insights to the understanding of individuals' wayfinding behaviours and reorientation processes.

The study is an experimental and theoretical analysis of the patterns of traffic flows and the possibilities of their distribution on urban street and road networks. It considers and analyses modern approaches that make it possible to improve traffic flow control mechanisms and traffic conditions on a street and road network. Based on the established internal relationships, the paper develops a model of the influence of factors on the level of their priority, which makes it possible to divide them by hierarchy levels and, accordingly, to observe the level of their impact on independent components. The scientific novelty of the research lies in the newly developed method of vehicle traffic control, which involves the distribution of traffic flows along the urban street and road network. The conclusions presented in the study represent scientific and methodological developments and applied recommendations that can be used in urban planning to improve the conditions of transport services in populated areas.

This paper proposes a proper compass adjustment method using only a GPS (or any other GNSS receiver) and a single visual reference to enhance the efficiency of compass adjustment. During compass adjustment, the ship proceeds on magnetic courses using a gyroscopic or satellite compass and considering magnetic declination. However, non-magnetic compasses are only compulsory for ships of 500 gross tonnage or upwards (SOLAS V/19.2.5.1). Many ships of less than 500 gross tonnage have only a magnetic compass to indicate heading. In these cases, a minimum of five leading lines or a minimum of five bearings of conspicuous and distant points or sun azimuths are necessary to adjust the compass. This makes compass adjustment more laborious and time consuming. To expedite this process, a reliable and practical method was developed to use the courses over ground given by a GNSS receiver and a single visual reference instead of the headings provided by a gyroscopic or satellite compass. The method is valid for all ships, but is primarily intended for those equipped with only a magnetic compass to indicate heading.

Thanks to the development of the real-time kinematic (RTK) algorithm and the emerging Global Navigation Satellite System (GNSS), especially for Galileo and BeiDou-3, reliable positioning accuracy for medium and long-baseline RTK became possible globally. Moreover, with the development of the GNSS receiver hardware, baseline length limitations due to radio-based communications are removed thanks to internet-based communication. In this work, single-baseline RTK, incorporated partial ambiguity resolution with troposphere and ionosphere weighting, using GPS (G), Galileo (E), BeiDou-3 (C3) and multi-GNSS (GE and GEC3), is conducted with real GNSS data of EUREF Permanent GNSS network under three different cutoff angles (10°, 20°, and 30°) for six different lengths of baselines (~50, ~150, ~250, ~350, ~450, and ~550 km). The results show that the multi-GNSS RTK solution significantly contributed to the positioning accuracy and convergence time of the single-system RTK solutions. Based on the results, non-available epoch-wise solutions for the high-degree cutoff angles are more obvious for the single-system RTK, whereas multi-GNSS solutions provide 100% solutions for each cutoff angle and baseline. The results indicate that instantaneous and a few epochs single-epoch ambiguity resolution is feasible for 50, 150, 250 and 350 km baseline lengths for multi-GNSS RTK. Based on the positioning results, horizontal–vertical positioning improvements of multi-GNSS RTK (GEC3) compared with the single-system GPS RTK are found as 50%–37%, 40%–35%, 55%–47%, 53%–54%, 57%–49% and 57%–49% for 50, 150, 250, 350, 450 and 550 km, respectively, under a 10° cutoff angle. For 20° and 30° cutoff angles, the accuracy improvements are much higher. The convergence time improvements (n/e/u) of multi-GNSS RTK (GEC3) compared with the single-system GPS RTK are found as 86/92/75%, 77/67/72%, 75/77/83%, 53/56/52%, 69/49/62%, and 52/45/39% for 50, 150, 250, 350, 450 and 550 km, respectively, under a 10° cutoff angle.

The present work explores the link between navigational processes and the experience of place by considering the case of Evenki reindeer herders and hunters. Our analysis shows how the idiosyncratic wayfinding methods of the Evenki result in a unique experience of place – a case that elucidates the important question of the impact of navigational processes on environmental experience, and that advances the debate between mental map theory and practical mastery theory in anthropology. We defend that their wayfinding methods – involving a particular gait, path networks, and vast hydrological and toponymical knowledge – allow the Evenki to navigate without a need for integrating egocentric and allocentric frames of reference. As a result, the Evenki experience themselves as free individuals moving through an environment that is alive and rife with possibility. This analysis reveals the ways in which wayfinding processes relying predominantly on route knowledge – as opposed to survey knowledge – affect environmental experience. Alternative methods of wayfinding can be seen as a form of resistance to the uniformisation of landscapes, and as a way of embracing the heterogeneity of space.

A relatively novel approach of autonomous navigation employing platform dynamics as the primary process model raises new implementational challenges. These are related to: (i) potential numerical instabilities during longer flights; (ii) the quality of model self-calibration and its applicability to different flights; (iii) the establishment of a global estimation methodology when handling different initialisation flight phases; and (iv) the possibility of reducing computational load through model simplification. We propose a unified strategy for handling different flight phases with a combination of factorisation and a partial Schmidt–Kalman approach. We then investigate the stability of the in-air initialisation and the suitability of reusing pre-calibrated model parameters with their correlations. Without GNSS updates, we suggest setting a subset of the state vector as ‘considered’ states within the filter to remove their estimation from the remaining observations. We support all propositions with new empirical evidence: first in model-parameter self-calibration via optimal smoothing and second through applying our methods on three test flights with dissimilar durations and geometries. Our experiments demonstrate a significant improvement in autonomous navigation quality for twelve different scenarios.

Complex domestic airspace requires collision risk models and monitoring tools suitable for arbitrary aircraft trajectories. This paper presents a new mathematically based collision risk approach that extends the International Civil Aviation Organisation (ICAO) models to full aircraft encounters based on real trajectory data. A new continuous time intervention model is presented, along with a position uncertainty propagation model that better reflects aircraft behaviour and allows generalisation to all trajectories to eliminate degenerate cases. The proposed risk model is computationally efficient compared to the models it is based on and can be applied to large-scale trajectory data. The utility of the model is demonstrated through a series of case studies using real aircraft trajectories.

Due to the exceptional complexity of the propulsion system (sails), square-riggers form a special group of sailing vessels. In modern pleasure and sport sailing, simple Bermuda (triangular) sailing rigging prevails, which is widely discussed in the literature, both in terms of theory and numerous experiments. The literature on the theory on square-riggers is, in turn, limited mainly to the description of good sailing practice developed over the centuries. Its important element was maximising vessel speed, but this discussion has not been documented by scientific research. This paper presents the significant parameters influencing the speed of a square-rigged sailing vessel and selects those which are the most important from the point of view of its maximisation. The paper also proposes methods and measurement systems which optimise selected parameters affecting the achievement of higher speeds. The paper describes the types of speeds of typical sailing vessels, provides a historical synthesis of sailing ships with respect to their speed, and presents a selection and description of the parameters affecting the speed of modern square-rigged vessels. The paper ends with a proposed method and measurement system for experimental research aiming at rigging optimisation in a square-rigged sailing vessel from the point of view of maximising its speed.

As no internationally agreed-upon method for determining safe speed values currently exists, collecting vast amounts of information on conventional ship behaviour could be used to train autonomous ship intelligence in determining safe speeds in different conditions. This requires speed data collected from conventional ships to resemble what can be described as safe speeds. To test this, the Automatic Identification System (AIS) and environmental data – namely visibility, mean wind speed and significant wave height – were collected and merged for two study areas in Norway in the period between 27 March 2014 and 1 January 2021. Regression analyses based on 47,490 unique vessel transits were conducted and supplemented by two graphical methods for revealing relationships between variables. Contrary to the contemporary understanding of safe speed, reduced visibility did not lead to significantly reduced transit speeds. Wind and waves caused a reduction in speed in the open ocean, but not in coastal waters. Transit speeds were lower in coastal waters than in the open ocean.

Autonomous vehicles rely on a combination of sensors for safe navigation around the world. For precise localisation, high-definition (HD) maps are used. These maps are a representation of the world containing information about objects on the road infrastructure. Currently, there are tens of HD map makers, however, no rigorous description of the requirements for the accuracy of HD maps has been published yet. This study fills the gap and offers a mathematical description of the minimum required accuracy for HD maps. In the first part, we identify factors that influence the quality of a map. Based on that, we proceed to present our solution for determining the minimum required accuracy for HD maps, both for static and dynamic models, and present a new formula for the minimum necessary accuracy for HD maps.

While quantum accelerometers sense with extremely low drift and low bias, their practical sensing capabilities face at least two limitations compared with classical accelerometers: a lower sample rate due to cold atom interrogation time; and a reduced dynamic range due to signal phase wrapping. In this paper, we propose a maximum likelihood probabilistic data fusion method, under which the actual phase of the quantum accelerometer can be unwrapped by fusing it with the output of a classical accelerometer on the platform. Consequently, the recovered measurement from the quantum accelerometer is used to estimate bias and drift of the classical accelerometer which is then removed from the system output. We demonstrate the enhanced error performance achieved by the proposed fusion method using a simulated 1D accelerometer precision test scenario. We conclude with a discussion on fusion error and potential solutions.

This study investigates whether the International Safety Management (ISM) Code remains an effective regulatory tool for the shipping industry by analysing maritime accident statistics published for the last 32 years by the Korea Maritime Safety Tribunal, 24 years by the International Maritime Organization (IMO) and seven years by the European Maritime Safety Agency. For this purpose, the data were divided into four groups: pre-ISM Code, in-transit, post-1 and post-2 periods, and tested by multilateral comparative analysis. The results show that the Korean merchant fleet has no improvement over the periods, whereas the world merchant fleet has a continual decrease in serious casualties. This study confirmed a trend of maritime accidents in recent years: fewer in number but heavier in damage. Conclusively, the results suggest that IMO and decision-makers in the maritime industry must review the ISM Code again and seek solutions to achieve the goal of continual improvement, as specified in the code.

A weather routing tool is presented based on forecasted weather data along the route and considering safety aspects. The tool aims to determine the optimal path for the minimisation of the fuel oil consumption, ensuring a safe passage. It is developed in MATLAB and considers detailed ship characteristics. Specifically, ship's motions and fuel oil consumption of the main engine during a potential path are estimated. For the latter, a physics-based model for a specific vessel is developed where tools of different level of detail are utilised to calculate the various resistance components. A speed management strategy along the route is specified as well as safety criteria representing acceptable limits of ship's responses. When the set criteria and constraints have been set, a genetic algorithm is used to find the optimal route by means of ship's heading or by considering both heading and ship's power settings as variables to minimise the fuel oil consumption. The search space of the algorithm lies within a predefined envelop, but still the evolutionary optimisation approach used has no pre-assigned values to any possible candidate waypoint.

There is an undeniable recognition that maritime cybersecurity risk management should involve process, technology, and people. However, thus far, most studies have focused on the technical and process aspects of maritime cybersecurity, more than on the human element. On a vessel, the Electronic Chart Display and Information System (ECDIS) is, amongst all the electronic devices on the bridge, a complex and indispensable maritime sociotechnical system that must consider both technical and human aspects. In the context of maritime cyber resilience, it is important to note that when developing strategies for maritime cybersecurity, one cannot only consider technical security measures and ignore human error, as this does not adhere to good cybersecurity practice. To address this, this study aims to identify the navigating officers’ responsibilities for ECDIS cybersecurity and find the human error probabilities during these tasks via the SLIM-based human reliability analysis method. The outputs of this study provide an insight for industrial policies and best practices, in ECDIS cybersecurity risk management in terms of the behavioural and cultural aspects of shipping.

Maritime navigation is a complex task involving the acquisition, analysis, and interpretation of information using seamanship, professional knowledge, and technology. As the maritime industry transitions towards maritime autonomous surface ships (MASS), there is an increasing gap between the operator and the technology. This paper explores a collision avoidance decision support system for navigation from the navigator's perspective. The system, developed by Wärtsilä, is called Advanced Intelligent Manoeuvring (AIM) and can generate suggestions for course or speed alterations based on data from surrounding traffic. Nineteen Swedish navigators completed three ship traffic scenarios with and without decision support. Qualitative data were collected using interviews and analysed with thematic analysis. The results show that the participants perceive the decision support system as an advisory tool to visualise how traffic situations could unfold, a task currently difficult for most navigators. This paper discusses the present and near future of maritime navigation, highlighting the benefits of automation, while remaining vigilant about the potential dangers.

E-navigation provides the opportunity to apply modern digital and other electronic enhancements to improve the safety and efficiency of maritime navigation. Under the broad banner of e-navigation, the International Hydrographic Organization's S-100 product specification framework is facilitating the establishment of a standard maritime data structure to enable a free-flowing exchange of navigation information between ships, ship-to-shore and shore-to-ship. There are currently over 30 S-100 based product specifications at various stages of development. For the data standard to be properly used, navigation software products must be capable of reading as well as comprehending the data format and content. To develop robust and stable software, the S-100 data models and product specifications must be consistent, accurate and interoperable in conveying various types of information. This paper describes the results of research on S-100 based product specifications from the viewpoint of developing maritime navigation software. In particular, issues related to software development for Electronic Chart Display Information System (ECDIS) and Vessel Traffic Service (VTS) are discussed, including appropriate data model analysis, processing of features, and symbols overlapping with other product specifications. Proposed solutions for some identified issues are presented.

In this paper, we propose a sea current relative navigation method using an interacting multiple model (IMM) filter with adaptive fading technique that can compensate an inaccurate sea current dynamics model. Due to the marine environment, the underwater vehicles largely depend on inertial navigation. Unfortunately, since its performance deteriorates with time, it is usually aided by another sensor. An electromagnetic-log (EM-log) and a Doppler velocity log (DVL), which are mainly used in marine navigation, provide relative velocity measurements to the sea currents, and hence require an accurate sea current dynamics model to fully utilise them. However, it is difficult to reflect the actual sea current changes with just a single fixed model, resulting in degraded overall navigation performance. Therefore, this paper proposes an IMM filter that can use multiple sea current dynamics models and has sub-filters designed with adaptive fading extended Kalman filter (AFEKF) to compensate for the mismodelling of sea current dynamics. The method is verified by simulation and shows a performance improvement comparable to the optimal filter.