There are limited studies on the use of ship domain as the safety criterion for ship navigation within confined waters. In this paper, a free-form ship domain has been developed empirically for navigation in confined waters. Two individual domains of an asymmetrical polygonal shape have been assumed around the own ship and target ship, and the size of the ship domains is assumed to be dynamically enlarged with increased ship speeds. The required safe distance modelled using the proposed ship domain takes into account dynamic changes in relative bearing and heading. The model is calibrated using the data of vessel movements in Singapore Strait and Singapore Port. An innovative process has been adopted that iteratively adjusts the parameters in the basic ship domain of a stationary ship; a speed function as well as a weight function to maximise the usage of the dataset. The resulting ship domain model compares well with existing ship domains for typical encounters, such as those of Fujii and Coldwell but represents non-typical encounters more effectively than existing models.

This paper presents the research for the development of a new travel aid to increase the independent mobility of blind and elderly travellers. This aid will build on the technologies of geographical information systems (GIS) and the Global Positioning System (GPS). The MOBIC Travel Aid (MOTA) consists of two interrelated components: the MOBIC Pre-journey System (MOPS) to assist users in planning journeys and the MOBIC Outdoor System (MOODS) to execute these plans by providing users with orientation and navigation assistance during journeys. The MOBIC travel aid is complementary to primary mobility aids such as the long cane or guide dog. Results of a study of user requirements are presented and their implications for the initial design of the system are discussed.

In this paper, a novel analytical framework for Dynamic Quaternion Ship Domain (DQSD) models has been initially proposed via the Quaternion Ship Domain (QSD) model structure. Unlike previous ship domains, the proposed DQSD model is able to capture essential subjectivity and objectivity of ship domains. To be specific, the significant characteristics are as follows:

1. (1) The proposed DQSD model is integrated by three independent submodels of ship, human and circumstance, which are determined by ship manoeuvrability, navigator's states, and navigation circumstance, respectively.

2. (2) The ship manoeuvrability derived from the MMG-type ship motion model is employed to establish the ship submodel which identifies the DQSD scale.

3. (3) A novel navigator reliability model is proposed to realize the human submodel which defines the ship domain shape with navigator ability, physical and mental states being input variables.

4. (4) In addition, visibility, wind force, wave and traffic congestion are incorporated into the circumstance submodel which is employed to zoom in or out of the DQSD-type ship domain.

Finally, the well-known Esso Osaka tanker model is used to conduct simulation studies on various typical stationary and dynamic situations, and comparative investigations with each other have been comprehensively analysed. Simulation results demonstrate that the DQSD model can capture critical dynamics of ship domains and undoubtedly be effective and superior to previous ship domains in terms of performance and accuracy.

The Automatic Identification System (AIS) has proven itself to be a valuable source for ship traffic information. Its introduction has reversed the previous situation with scarcity of precise data from ship traffic and has instead posed the reverse challenge of coping with an overabundance of data. The number of time-series available for ship traffic and manoeuvring analysis has increased from tens, or hundreds, to several thousands. Sifting through these data manually, either to find the salient features of traffic, or to provide statistical distributions of decision variables is an extremely time consuming procedure. In this paper we present the results of applying computer vision techniques to this problem and show how it is possible to automatically separate AIS data in order to obtain traffic statistics and prevailing features down to the scale of individual manoeuvres and how this procedure enables the production of a simplified ship traffic model.

Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands from January to February 2015. The results indicate that the algorithms can effectively improve the quality of AIS trajectories.

This paper describes the features of an extended Kalman filter algorithm designed to support the navigational function of a real-time vehicle performance and emissions monitoring system currently under development. The Kalman filter is used to process global positioning system (GPS) data enhanced with dead reckoning (DR) in an integrated mode, to provide continuous positioning in built-up areas. The dynamic model and filter algorithms are discussed in detail, followed by the findings based on computer simulations and a limited field trial carried out in the Greater London area. The results demonstrate that use of the extended Kalman filter algorithm enables the integrated system employing GPS and low cost DR devices to meet the required navigation performance of the device under development.

Most of the existing typical ship domains have been comprehensively reviewed and classified. Most of these ship domains are described in a geometrical manner that is difficult to apply to practices and simulations in marine traffic engineering. According to different types of geometrical ship domains, we have proposed mathematical models, based on which a unified analytical framework has been established. It is feasible and practical for the analytical models to be applied to the assessment of navigational safety, collision avoidance and trajectory planning, etc. Finally, some computer simulations and comparative studies of the proposed domain model have been presented and the simulation results show that the uniform analytical framework for ship domains is effective and identical to the original geometrical ones. It should be noted that the analytical domain models could be directly applied in any collision risk, collision avoidance or VTS system while the geometrical ones would be more illustrative but less practical or analytical.

To achieve high Strapdown Inertial Navigation System (SINS) alignment accuracy within a short period of time is still a challenging issue for underwater vehicles. In this paper, a new SINS initial alignment scheme aided by the velocity derived from Doppler Velocity Log (DVL) is proposed to solve this problem. In the stage of the coarse alignment, the velocity of DVL is employed to reduce the impact of the linear motion. With a backtracking framework, the fine alignment runs with the data recorded during the process of the coarse alignment and thus will speed up the overall alignment process. In addition, by using this new scheme, it is equivalent to length the alignment time for both coarse and fine alignments, so the accuracy of the alignments will be improved. In order to reduce the volume of the data that has to be recorded, a new model for SINS fine alignment is derived in the inertial reference frame which makes it feasible for real time applications. The experimental results are presented for both unaided static and in-motion alignment using DVL aiding. It is clearly shown that the proposed method meets the requirement of SINS alignment for underwater vehicles.

This paper pertains to the reduction of the effects of measurement errors in rate gyros used for tracking, recording, or monitoring the position of persons walking indoors. In such applications, bias drift and other gyro errors can degrade accuracy within minutes. To overcome this problem we developed the Heuristic Drift Elimination (HDE) method, that effectively corrects bias drift and other slow-changing errors. HDE works by making assumptions about walking in structured, indoor environments. The paper explains the heuristic assumptions and the HDE method, and shows experimental results. In typical applications, HDE maintains near-zero heading errors in walks of unlimited duration.

Accurate vehicle location is essential for various applications in the field of intelligent transportation systems (ITS). Existing vehicle location systems rely on multiple positioning sensors and powerful computing devices to execute complex map matching algorithms. There exists a strong need for exploring a solution for vehicle location that relies on a GPS receiver as the sole means of positioning and does not require complex computations. Towards this end, the error characteristics of the GPS signal were studied through the analysis of GPS data collected during test drives. Based on the inferences drawn and a simple fuzzy rule set, a novel yet simple map matching algorithm was developed. Due to the difficulties in testing the algorithm through on-road trials, a simulation environment that is capable of reproducing the field conditions in the laboratory was developed. Simulation results confirm that the proposed algorithm overcomes many of the inadequacies of the existing methods and is capable of achieving high accuracy with minimal computational requirements.

The paper pertains to the reduction of measurement errors in gyroscopes used for tracking the position of walking persons. Such tracking systems commonly use inertial or other means to measure distance travelled, and one or more gyros to measure changes in heading. MEMS-type gyros or IMUs are best suited for this task because of their small size and low weight. However, these gyros have large drift rates and can be sensitive to accelerations. The Heuristic Drift Reduction (HDR) method presented in this paper estimates the drift component and eliminates it, reducing heading errors by almost one order of magnitude.

Developing adequate ship domain models may significantly benefit vessel navigation safety. In essence, navigation safety is collectively affected by the navigable waterway condition, the size and shape of the ship, and operators' skills. The existing ship domains mainly use constant values for the model input parameters, making them incapable of handling site-specific conditions. This study proposes dynamic ship domain models that take into consideration navigable waterway conditions, ship behaviours, ship types and sizes, and operators' skills in a holistic manner. Specifically, the conditions of restricted waterways are classified into navigating along the channel, crossing the channel, joining another flow and turning. The ship types considered include ships that transport non-hazardous goods and Liquid Natural Gas (LNG) ships that are in need of additional security zones. A computational experiment is conducted for model application using data on water channel design and ship traffic volumes related to navigating along the channel, joining another flow and turning. Comparisons of results obtained between the proposed dynamic models with real ship traffic counts reveal that the proposed models could achieve a higher level of accuracy in estimating the capacity of restricted water channels. It therefore could potentially deliver safety enhancements of waterway transportation.

This paper considers the estimation of the process state and noise parameters when the statistics of the process and measurement noise are unknown or time varying in the integration system. An adaptive Kalman Filter (AKF) with a recursive noise estimator that is based on maximum a posteriori estimation and one-step smoothing filtering is proposed, and the AKF can provide accurate noise statistical parameters for the Kalman filter in real-time. An exponentially weighted fading memory method is introduced to increase the weights of the recent innovations when the noise statistics are time varying. Also, the innovation covariances within a moving window are averaged to correct the noise statistics estimator. Experiments on the integrated Strapdown Inertial Navigation System (SINS)/ Doppler Velocity Log (DVL) system show that the proposed AKF improves the estimation accuracy effectively and the AKF is robust in the presence of vigorous-manoeuvres and rough sea conditions.

In this paper, we discuss the grounding of the Royal Majesty, reconstructed from the perspective of the crew. The aim is particularly to understand the role of automation in shaping crew assessments and actions. Automation is often introduced because of quantitative promises that: it will reduce human error; reduce workload; and increase efficiency. But as demonstrated by the Royal Majesty, as well as by numerous research results, automation has qualitative consequences for human work and safety, and does not simply replace human work with machine work. Automation changes the task it was meant to support; it creates new error pathways, shifts consequences of error further into the future and delays opportunities for error detection and recovery. By going through the sequence of events that preceded the grounding of the Royal Majesty, we highlight the role that automation plays in the success and failure of navigation today. We then point to future directions on how to make automated systems into better team players.

Maritime anomaly detection can improve the situational awareness of vessel traffic supervisors and reduce maritime accidents. In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a recurrent neural network is presented. In the method presented, the parameters of the DBSCAN algorithm were determined through statistical analysis, and the results of clustering were taken as the traffic patterns to train a recurrent neural network composed of Long Short-Term Memory (LSTM) units. The neural network was applied as a vessel trajectory predictor to conduct real-time maritime anomaly detection. Based on data from the Chinese Zhoushan Islands, experiments verified the applicability of the proposed method. The results show that the proposed method can detect anomalous behaviours of a vessel regarding speed, course and route quickly.

Integer ambiguity resolution in Precise Point Positioning (PPP) can shorten convergence time and improve accuracy significantly. Uncalibrated Fractional Offsets (UFOs) originating in the satellites destroy the integer nature of carrier phase ambiguities observed at a single station. Several methods have been developed to estimate UFO information from a reference network for PPP ambiguity resolution. In this paper, we present a new approach for estimating Zero-Differenced (ZD) UFOs via float ZD ambiguities from a reference network. In this new approach, UFOs for receivers and satellites are estimated in an integrated adjustment with integer ambiguities being resolved sequentially, so that UFOs of higher quality can be achieved. The float ZD ambiguities used in the estimation can be from network or PPP solutions. Using those from PPP solutions enables the time-consuming clock estimations and the UFO estimations to be carried out separately, so that UFOs can be estimated more reliably from a much denser network. In this paper, a data processing procedure, from the estimation of UFOs through to PPP ambiguity fixing, is designed and proposed. The approach is validated with several data sets in various scenarios. The results show that satellite UFOs can be estimated precisely and reliably by using the observations from a reference network, in which the station spacing could be up to thousands of kilometres. With 30 minutes of observations, a PPP solution with fixed integer ambiguities is achievable and the positioning accuracy can be improved significantly when compared with its float solution.

The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.

Navigational safety analysis relying on collision statistics is often hampered because of the low number of observations. A promising alternative approach that overcomes this problem is proposed in this paper. By analyzing critical vessel interactions this approach proactively measures collision risk in port waters. The proposed method is illustrated for quantitative measurement of collision risks in Singapore port fairways, and validated by examining correlations between the measured risks with those perceived by pilots. This method is an ethically appealing alternative to the collision-based analysis for fast, reliable and effective safety assessment, thus possessing great potential for managing collision risks in port waters.

This paper argues for the use of landmarks (for example, traffic lights, churches, petrol stations) within the turn-by-turn visual and voice directions given by in-vehicle navigation systems. Such prominent features of the driving environment are consistent with basic human navigational strategies, are valued by drivers, and have been shown significantly to improve the usability of electronic in-car navigational aids. For future systems actively to include such information, it is critical that (a) only ‘good’ landmarks are used, (b) such landmarks are presented to the driver in the most appropriate way, and (c) the practical needs of industry are fully accounted for.

With more satellite systems becoming available there is currently a need for Receiver Autonomous Integrity Monitoring (RAIM) to exclude multiple outliers. While the single outlier test can be applied iteratively, in the field of statistics robust methods are preferred when multiple outliers exist. This study compares the outlier test and numerous robust methods with simulated GPS measurements to identify which methods have the greatest ability to correctly exclude outliers. It was found that no method could correctly exclude outliers 100% of the time. However, for a single outlier the outlier test achieved the highest rates of correct exclusion followed by the MM-estimator and the L1-norm. As the number of outliers increased MM-estimators and the L1-norm obtained the highest rates of normal exclusion, which were up to ten percent higher than the outlier test.