The Shore Control Centre (SCC) is being developed and tested as an autonomous ship vessel with remote control. However, since the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW) regulation's competency standard has yet to be altered, it must be revised and modified. Therefore, this study aims to define the competency of remote operators on an autonomous ship from ashore and develop a conceptual model of remote operators' competency. This study used both primary data by interviewing executives from four groups of maritime experts, and secondary data from academic databases, IMO, classification society and maritime companies. Academic databases are employed to conclude the academicians' view on remote operators’ (ROs’) competency and the other data sources are used to conclude the industrial view on the RO's competency. The content analysis technique was used to determine the presence of keywords or concepts from secondary data and develop a conceptual model. The study's findings present four main dimensions to indicate the development of future training and development programs for RO officers: navigation, cargo handling and stowage, controlling the ship's operation and care for persons onboard, and information technology; and present 45 competencies of ROs for managing autonomous ships from ashore, which a conceptual model can explain.

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.

Official interpretations of Doppler shifts from the final satellite communications of missing Malaysian Airlines MH370 were based on a motion-decoupled ‘Up-Down model’. That model predicted an uncontrolled high-speed gravitationally accelerated dive following fuel starvation. Here, I challenge that model using a more-realistic motion-coupled ‘Declination model’. Aerial, satellite and underwater searches failed to find the predicted official violent crash-site near the 7th arc. Meticulous re-examination of debris damage by air-crash investigator Larry Vance concluded that the aircraft glide-landed under power with extended wing-flaps. The trailing-edges were then damaged, broke off their mountings, flailing about and retracted along the guides to cause the observed wing-flap damage. Larry's conclusions complement interpretations from the ‘Declination model’ which we demonstrate here with three example flight tracks. Our revised Doppler-shift analyses support the hypothesis of a controlled eastward descent. We conclude that the official theory of fuel starvation and a high-speed dive are fundamentally flawed.

The geostationary orbit (GEO) belt hosts a substantial number of high-value satellites, making the study of autonomous navigation within this area significant. Features of autonomous operations such as patrolling the GEO belt and frequent manoeuvres at a certain location make real-time positioning using the Global Navigation Satellite System (GNSS) valuable. This paper studies the performance of positioning with GNSS considering main lobe and side lobe signals at different longitudes in the GEO belt. The research delves into the visibility and Position Dilution of Precision (PDOP) across the GEO belt, analysing the performance of the Global Positioning System (GPS), GLObalnaya NAvigatsionnaya Sputnikovaya Sistema (GLONASS) in Russian, BeiDou Navigation Satellite System (BDS), Galileo Navigation Satellite System (Galileo) and multi-systems. In particular, this paper investigates the impact of asymmetric constellations of mixed GEO, Inclined Geosynchronous Orbit (IGSO) and Medium Earth Orbit (MEO) satellites. The study reveals that BDS hybrid constellation provides long-term stable signal coverage over the GEO space above North America and Atlantic Ocean, where GEO signals are more sustainable while IGSO signals have wider coverage. This advantage positions BDS favourably in terms of performance in these regions.

Indoor navigation for micro aerial vehicles (MAVs) is challenging in GPS signal-obstructed indoor corridor environments. Position and heading estimation for a MAV is required to navigate without colliding with obstacles. The connected components algorithm and k-means clustering algorithm have been integrated for line and vanishing point detection in the corridor image frames to estimate the position and heading of the MAV. The position of the vanishing point indicates the position of the MAV (centre, left or right) in the corridor. Furthermore, the Euclidean distance between the image centre and mid-pixel coordinates at the last row of the image and the detected vanishing point pixel coordinates in the successive corridor image frames are used to compute the heading of the MAV. When the MAV deviates from the corridor centre, the position and heading measurement can send a suitable control signal to the MAV and align the MAV at the centre of the corridor. When compared with a grid-based vanishing point detection method heading accuracy of ±1⋅5°, the k-means clustering-based vanishing point detection is suitable for real-time heading measurement for indoor MAVs with an accuracy of ±0⋅5°.

In previous research, several computational methods have been proposed to analyse the navigation, transportation safety and collision risks of maritime vessels. The objective of this study is to use Automatic Identification System (AIS) data to assess the collision risk between two vessels before an actual collision occurs. We introduce the concept of an angle interval in the model to enable real-time response to vessel collision risks. When predicting collision risks, we consider factors such as relative distance, relative velocity and phase between the vessels. Lastly, the collision risk is divided into different regions and represented by different colours. The green region represents a low-risk area, the yellow region serves as a cautionary zone and the red region indicates a high-alert zone. If a signal enters the red region, the vessel's control system will automatically intervene and initiate evasive manoeuvres. This reactive mechanism enhances the safety of vessel operations, ensuring the implementation of effective collision avoidance measures.

By simultaneously estimating satellite clock drifts (SCDs) as either constant parameters or piece-wise parameters, we present an improved integrated orbit determination and time synchronization approach for BDS-3 satellites with raw inter-satellite link (ISL) observations. Experiments with L-band data from seven monitoring stations in China and ISL data from eight satellites of the third-generation Beidou Navigation Satellite System (BDS-3) were carried out and the two SCD estimation strategies are validated. It is demonstrated that, with SCDs estimated, the quality of orbits and clock offsets is comparable to those with SCDs corrected using predicted values. The accuracy of the estimated orbits and clocks are up to 0.019 m (radial) and 0.095 ns, respectively, with improvements of 95% and 90%, when compared with the results using the L-band data alone. It is also demonstrated that estimating SCDs time slice by time slice is slightly worse in accuracy but superior in coping with possible frequency jump of satellite clocks.

Missed detection probability is a critical metric for the integrity performance of receiver autonomous integrity monitoring (RAIM) in the presence of faults. The traditional missed detection probability evaluation method for RAIM is limited by impractical time consumption because of the absence of accurate searching interval for the magnitude of a worst-case fault. To address this issue, the searching interval for the magnitude of a worst-case fault is constructed by the combination of minimum detectable magnitude and minimum hazardous magnitude, and the searching interval adjustment is designed to avoid the absence of worst-case fault magnitude so that the maximum missed detection probability can be accurately evaluated. The simulation result indicates that the proposed method can achieve higher accuracy for the worst-case fault magnitude searching. Furthermore, the accuracy of worldwide evaluated missed detection rate can achieve an improvement of 57·66% at most by the proposed method for the different classical RAIM algorithms.

During the 1519–1522 Magellan expedition, the astronomer Andrés de San Martín made two remarkably accurate longitude measurements, an order of magnitude better than what was typical for the 16th century. How he managed to do so remained shrouded in mystery for the past 500 years. Using modern ephemerides, we have retraced San Martín's observations and calculated their error signatures, clarifying the method he used (a simplified version of lunar distances) and why two out of his six measurements were accurate (a rather fortuitous cancellation of errors). It would be rash to dismiss San Martín's work as sheer luck though, as he was an exceedingly rare combination of a capable astronomer and a knowledgeable mariner.

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.

Accidents are a prevalent feature of working in the maritime industry. While studies have shown to what extent accidents and fatalities have occurred, the current research has generally been limited to commercial shipping. There is nearly no academic research focusing on the safety issues in the superyacht industry. This paper analyses the importance of promoting safety culture in the superyacht industry, the role of maritime legislation in maintaining safety and the role of Port State Control in ensuring all legislation is implemented. It aims to provide a critical examination of safety culture in the superyacht industry and evaluate the appropriateness for further measures to ensure safe working practices. It found out that while some superyachts do maintain an effective safety system, there remains almost 50% of the investigated fleet that do not promote the desired safety culture. It becomes evident that complacency and poor education contribute to the reduced and limited safety culture. The lack of education and awareness is demonstrated when the study shows individuals believing they maintain good safety practices but still admitting to taking various life-threatening risks.

Unmanned aerial vehicles (UAVs) have recently been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, UAVs can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative UAVs will offer diverse interpretations which support a multiperspective view of the scene. Due to the diverse interpretations of UAVs usually deviating, UAVs require a consensus interpretation for the scenario. This study presents an original consensus-based method to pilot multi-UAV systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Taylor series are used to describe the fuzzy nonlinear plant and derive the stability analysis using polynomial functions, which have the representations $V(x )={m_{\textrm{1} \le l \le N}}({{V_\textrm{l}}(x )} )$ and ${V_l}(x )={x^T}{P_l}(x )x$. Due to the fact that the ${\dot{P}_l}(x )$ in ${\dot{V}_l}(x )={\dot{x}^T}{P_l}(x )x + {x^T}{\dot{P}_l}(x )x + {x^T}{P_l}(x )\dot{x}$ will yield intricate terms to ensure a stability criterion, we aim to avoid these kinds of issues by proposing a polynomial homogeneous framework and using Euler's functions for homogeneous systems. First, this method permits each UAV to establish high-level conditions from the probed events via a fuzzy-based aggregation event. The evaluated consensus indicates how suitable is the scenario collective interpretation for every UAV perspective.

Vessel trajectories from the Automatic Identification System (AIS) play an important role in maritime traffic management, but a drawback is the huge amount of memory occupation which thus results in a low speed of data acquisition in maritime applications due to a large number of scattered data. This paper proposes a novel online vessel trajectory compression method based on the Improved Open Window (IOPW) algorithm. The proposed method compresses vessel trajectory instantly according to vessel coordinates along with a timestamp driven by the AIS data. In particular, we adopt the weighted Euclidean distance (WED), fusing the perpendicular Euclidean distance (PED) and synchronous Euclidean distance (SED) in IOPW to improve the robustness. The realistic AIS-based vessel trajectories are used to illustrate the proposed model by comparing it with five traditional trajectory compression methods. The experimental results reveal that the proposed method could effectively maintain the important trajectory features and significantly reduce the rate of distance loss during the online compression of vessel trajectories.

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.

Maritime traffic risk is increasing rapidly with the growth of marine traffic volume and construction of marine facilities, water bridges, port development, marine wind farm, etc. Given this emerging trend, this paper presents a bibliometric analysis and mapping of the broad academic literature related to maritime traffic safety, focusing on the influences of international collaborations and knowledge sources on the developments of this research domain. To identify trends, patterns and the knowledge distribution of the research on maritime traffic safety, the visualisation of similarities (VOS) viewer software, the bibliometric analysis, and scientometric mapping of the literature have been performed from the perspectives of publication and citation distribution over time, leading authors, countries (regions), institutions, the corresponding collaboration networks, most cited publications and references, focused research fields and topics, research trend evolution over time, etc. The paper provides a comprehensive and quantitative overview and significant picture representation of the domain's leading and evolutionary trends by employing specific aforementioned bibliometric analysis factors. In addition, by reviewing the evolutionary trends of the journals and the proposed investigated factors, such as the influential works, main research topics, and the research frontiers, this paper reveals the scientific literature's main research objectives and directions that could be addressed and explored in future studies.

In this paper, a complete introduction to the dead reckoning navigation technique is offered after a discussion of the many forms of navigation, and the benefits and drawbacks associated with each of those types of navigation. After that, the dead reckoning navigation solution is used as an option that is both low-cost and makes use of the sophisticated equations that are used by the system. Moreover, to achieve the highest level of accuracy in navigation, an investigation of navigation errors caused by dead reckoning is calculated. Employing the suggested dead reckoning navigation system, the final position of an underwater vehicle can be established with a high degree of accuracy by using experimental data (from sensors) and the uncertainties that are associated with the system. Finally, to illustrate the correctness of the dead reckoning navigation process, the system error analysis as uncertainty that was carried out using experimental data using the dead reckoning navigation technique is compared with GPS data.

The release of GNSS raw data on Android smartphones provides the potential for high-precision smartphone positioning using multi-constellation and multi-frequency signals. However, severe multipath and low observation quality in kinematic environments make double-differenced uncombined ambiguities difficult to resolve reliably. To address this, the paper proposes an improved wide-lane (WL) integer ambiguity resolution (IAR) method that combines integer rounding and the Least-Square AMBiguity Decorrelation Adjustment (LAMBDA) methods. The proposed method achieved fix rates of 57% to 70% in challenging environments, with an average improvement of 7 · 7% in horizontal positioning accuracy compared to the float solution. The traditional partial integer rounding method only improved accuracy by 1 · 1%.

With the development of GNSS (Global Navigation Satellite System), LEO (Low Earth Orbit) systems are adopted to enhance the system performance of GNSS. The signal Doppler of the LEO satellite is seven to nine times that of GNSS signals, which benefits positioning performance but leads to high acquisition complexity. This paper proposes the combination of a CSS (Chirp Spread Spectrum) marker and the main body of traditional modulation methods for high dynamic application. The acquisition calculation complexity and mean acquisition time of the proposed signal are analysed and compared with the traditional signal. The result shows that the acquisition calculation complexity is just 26 % of the traditional signal under the parameters considered and the mean acquisition time of the proposed signal is also lower than the traditional signal. Hence, the proposed signal is able to decrease the mean acquisition time of the receiver under the constraint of calculation complexity and should be adopted for LEO high dynamic application.

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 proposes a switched model to improve the estimation of Euler angles and decrease the inertial navigation system (INS) error, when the centrifugal acceleration occurs. Depending on the situation, one of the subsystems of the proposed switched model is activated for the estimation procedure. During global positioning system (GPS) outages, an extended Kalman filter (EKF) operates in the prediction mode and corrects the INS information, based on the system error model. Compared with previous works, the main advantages of the proposed switched-based adaptive EKF (SAEKF) method are (i) elimination of INS error, during the centrifugal acceleration, and (ii) high accuracy in estimating the attitude and positioning, particularly during GPS outages. To validate the efficiency of the proposed method in various trajectories, an experimental flight test is performed and discussed, involving a microelectromechanical (MEMS)-based INS. The comparative study shows that the proposed method considerably improves the accuracy in various scenarios.