Over the past few decades, the number of liquefied natural gas (LNG) ships and terminals has been increasing, playing an important role in global clean energy transportation. However, the traffic capacity of LNG shipping in port areas is limited because of its high safety requirements. In view of this problem, a novel model is proposed to study the ship traffic in a port area by combining cellular automaton (CA) and multi-agent methods. Taking the CNOOC Tianjin LNG Terminal as an example, the ship traffic in Tianjin Port is simulated. Based on the simulation results, the LNG ship traffic capacity and its impact on the general shipping traffic flow under different special traffic rules are obtained. This model can provide theoretical support for optimising the port traffic organisation for LNG ships.

Coastal surveillance video helps officials to obtain on-site visual information on maritime traffic situations, which benefits building up the maritime transportation detection infrastructure. The previous ship detection methods focused on detecting distant small ships in maritime videos, with less attention paid to the task of ship detection from coastal surveillance video. To address this challenge, a novel framework is proposed to detect ships from coastal maritime images in three typical traffic situations in three consecutive steps. First the Canny detector is introduced to determine the potential ship edges in each maritime frame. Then, the self-adaptive Gaussian descriptor is employed to accurately rule out noisy edges. Finally, the morphology operator is developed to link the detected separated edges to connected ship contours. The model's performance is tested under three typical maritime traffic situations. The experimental results show that the proposed ship detector achieved satisfactory performance (in terms of precision, accuracy and time cost) compared with other state-of-the-art algorithms. The findings of the study offer the potential of providing real-time visual traffic information to maritime regulators, which is crucial for the development of intelligent maritime transportation.

Colour is widely utilised as a visual coding system in visual search, but its application under vibration conditions (e.g., in various vehicles) has not been fully explored. This study was designed to examine the effect of colour combinations on performance of visual search tasks conducted in vibration conditions. Forty-eight university students participated in an experiment where they were required to identify target type and location under 24 colour combinations (half in negative polarity and half in positive polarity) and three vibration conditions (static, low, and high). The findings showed that vibration did not significantly affect performance, perceptions, or physiological aspects. Colour combination significantly affected response time, and the participants preferred colour combinations that had the potential to produce better performance. Colour combinations with negative polarity (e.g., yellow on black and white on black) are recommended for presenting search interfaces. These findings are of importance in human–computer interface designs for information display under vibration conditions.

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.

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.

To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

This paper presents a formulation of snapshot positioning as a mixed-integer least-squares problem. In snapshot positioning, one estimates a position from code-phase (and possibly Doppler-shift) observations of global navigation satellite system (GNSS) signals without knowing the time of departure (timestamp) of the codes. Solving the problem allows a receiver to determine a fix from short radio-frequency snapshots missing the timestamp information embedded in the GNSS data stream. This is used to reduce the time to first fix in some receivers, and it is used in certain wildlife trackers. This paper presents two new formulations of the problem and an algorithm that solves the resulting mixed-integer least-squares problems. We also show that the new formulations can produce fixes even with huge initial errors, much larger than permitted in Van Diggelen's widely-cited coarse-time navigation method.

Introducing the notion of a pseudoloxodrome, we generalise a single-heading navigation to conformally flat Riemannian manifolds, under the action of a perturbing vector field (wind, current) of arbitrary force. The findings are applied to time-optimal navigation with the use of the Euler–Lagrange equations. We refer to the Zermelo navigation problem admitting space and time dependence of both a perturbation and a ship's speed. The necessary conditions for single-heading time-optimal navigation are obtained and the pseudoloxodromes of minimum and maximum time are discussed. Furthermore, we describe winds which yield the pseudoloxodromic and loxodromic time extremals. Our research is also illustrated with the examples in dimension two emphasising the single-heading solutions among the time-optimal trajectories in the presence of some space-dependent winds.

The year 2020 marked the 95th anniversary of Francesco De Pinedo and Ernesto Campanelli's record-breaking flight of 55,000 km, from Italy to Australia, Japan and back, in a seaplane named Gennariello. Their achievement was lauded worldwide, especially in Australia, where the press reported on it intensively. This paper reconstructs the story of the flight by analysing the Australian press accounts and De Pinedo's diary, to understand how the Australian public perceived the event. It investigates the aviators’ arrival in Perth, Adelaide and Melbourne, where their popularity was greatest and where the local Italian communities enthusiastically welcomed them. The analysis shows that the flight engendered increased public interest and paid dividends in terms of image for the commercial companies involved, while fascism exploited it to display its progress in aviation. The paper concludes by exploring the legacy of the endeavour in modern Italy and Australia, emphasising the differences between the countries.

This paper introduces a power series based method for attitude reconstruction from triad orthogonal strap-down gyros. The method is implemented and validated using quaternions and direction cosine matrix in single and double precision implementation forms. It is supposed that data from gyros are sampled with high frequency and a fitted polynomial is used for an analytical description of the angular velocity vector. The method is compared with the well-known Taylor series approach, and the stability of the coefficients’ norm in higher-order terms for both methods is analysed. It is shown that the norm of quaternions’ derivatives in the Taylor series is bigger than the equivalent terms coefficients in the power series. In the proposed method, more terms can be used in the power series before the saturation of the coefficients and the error of the proposed method is less than that for other methods. The numerical results show that the application of the proposed method with quaternions performs better than other methods. The method is robust with respect to the noise of the sensors and has a low computational load compared with other methods.

Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

This paper proposes a novel method of error mitigation for stereo visual odometry (VO) applied in land vehicles. A non-holonomic constraint (NHC), which imposes physical constraint to the rightward velocity of a land vehicle, is implemented as an observation in an extended Kalman filter (EKF) to reduce the drift of stereo VO. The EKF state vector includes position errors in an Earth-centred, Earth-fixed (ECEF) frame, velocity errors in the camera frame, angular rate errors and attitude errors. All the related equations are described and presented in detail. In this approach, no additional sensors are used but NHC, namely velocity constraint in the right direction , is applied as an external measurement to improve the accuracy. Tests are conducted with the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets. Results show that the relative horizontal positioning error improved from 0⋅63% to 0⋅22% on average with the application of the velocity constraints. The maximum and root mean square of the horizontal error with velocity constraints are both reduced to less than half of the error with stand-alone stereo VO.

The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

‘Close-quarters situation’ is a term used in the International Regulations for Preventing Collisions at Sea. As the term is not precisely defined, this paper analyses the interpretations and definitions of the term by various authors or courts, based on judicial processes and judgments. In the end, the authors suggest their own definition of the term ‘close-quarters situation’. Knowing the minimum distance from another ship and the time to the closest point of approach at which collision may still be avoided by one's own manoeuvre is information that every ship's officer needs to know. In accordance with the proposed definition of the term ‘close-quarters situation’, minimum distances between ships and time to the closest point of approach in which the ship can still take action to avoid a collision by its own manoeuvring are determined by means of simulations on a navigational simulator. A total of 168 simulations were performed with three fine-form vessel sizes and three full-form vessel sizes. Due to the extensive amount of data, the paper presents the results for one vessel only.

Spoofing is a kind of deliberate interference that aims to manipulate global navigation satellite system (GNSS) receivers into counterfeit position solutions. Conventional anti-spoofing methods are implemented prior to the calculation of the position solution, depending on the specific spoofing attack mechanisms. The paper presents a spoofing detection and mitigation method implemented in the position domain. The proposed method projects the correlograms of the visible satellites to a position-clock bias domain to construct the position domain projected correlogram. P(Y) code signatures retrieved from a reference station receiver are used to identify the counterfeit position solution and remove it from the victim receiver. Compared with the conventional single-channel spoofing detection technique, the proposed anti-spoofing method is more robust against thermal noise by combining the energy from multiple satellites. Detailed mathematical derivation of the statistical characteristics of this method is presented. Its effectiveness is validated using a realistic dataset generated by a Spirent GNSS simulator and NordNav wideband front-end. Results show that the proposed algorithm is capable of not only detecting a spoofing attack but also removing the spoofing effect from the victim receiver.

As tugboats interact very closely with ships in restricted waters, the possibility of accidents increases in these operations. Despite the high accident possibility, there is a gap in studies on tugboat accidents. This study aims to analyse accidents involving tugboats using data mining. For this purpose, a tugboat accidents dataset consisting of a total of 496 accident records for the period from 2008 to 2019 was collected. Logistic regression and decision tree algorithms were implemented to the dataset. The results revealed that tugboat propulsion type is the most important and influential factor in the severity of tugboat accidents. The inferences drawn from these results could be beneficial for tugboat operators and port authorities in enhancing their awareness of the factors affecting tugboat accidents. In addition, the outputs of this study can be a reference for management units in developing strategies for preventing tugboat accidents and can also be used in effective planning for practicable prevention programmes and practices.

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.

Global navigation satellite system (GNSS) receivers meet numerous challenges in a high-orbit environment, including weak and discontinuous signal, and time-varying strength. To resolve these issues and enhance reliability, an innovative adaptive vector tracking loop (VTL) scheme is proposed. Non-linear models of the VTL filter are established to calculate code phase and carrier frequency errors accurately. Based on this, a deep analysis has been developed on the measurement noise. To reduce the impact of the interdependent noises among channels in VTL, an adaptive VTL algorithm assisted by the variational Bayesian (VB) learning network is proposed to estimate the measurement noise and maintain the error convergence in the time-varying noise or signal outage conditions. Further, the implementation steps of the adaptive algorithm have been designed in detail. In particular, the carrier-to-noise power ratio (C/N0) estimation method is further employed to update the a prior probability density in case of change of tracking satellite. The simulation results indicate that the proposed VTL scheme with VB algorithm is a promising method to improve the accuracy and reliability of GNSS receivers significantly under a high-orbit degraded signal environment.

This paper presents a novel estimation method for coarse alignment of a marine strapdown inertial navigation system (SINS) under mooring conditions. The properties of gravitational motion are used to improve the accuracy of coarse alignment. The parametric motion of gravitational apparent is a circle that is on the surface of a sphere. The location of this parametric circle is dependent on the definition of the reference frames and the initial angles of SINS. In the method proposed here the initial direct cosine matrix is calculated only from the location of the gravity motion parametric circle. The novelty of this paper is to provide a new method for estimating the gravity motion trajectory in inertial frame, as well as direct extraction of the initial direct cosine matrix from this estimated trajectory. Simulation and testing show that the proposed method is suitable for coarse alignment in mooring conditions.

This paper describes a camera simulation framework for validating machine vision algorithms under general airborne camera imperfections. Lens distortion, image delay, rolling shutter, motion blur, interlacing, vignetting, image noise, and light level are modelled. This is the first simulation that considers all temporal distortions jointly, along with static lens distortions in an online manner. Several innovations are proposed including a motion tracking system allowing the camera to follow the flight log with eligible derivatives. A reverse pipeline, relating each pixel in the output image to pixels in the ideal input image, is developed. It is shown that the inverse lens distortion model and the inverse temporal distortion models are decoupled in this way. A short-time pixel displacement model is proposed to solve for temporal distortions (i.e. delay, rolling shutter, motion blur, and interlacing). Evaluation is done by several means including regenerating an airborne dataset, regenerating the camera path on a calibration pattern, and evaluating the ability of the time displacement model to predict other frames. Qualitative evaluations are also made.