Vessel traffic in the Arctic is expanding in volume both within and transiting the region, yet the infrastructure necessary to support modern ship navigation is lacking. This includes aids to navigation such as buoys and beacons that can be difficult to place and maintain in this hostile environment that stretches across vast distances. The results of research are described which determine whether virtual electronic Aids to Navigation (eAtoN) existing entirely as digital information objects can overcome the practical limitations of physical aids to navigation (AtoN) and Automatic Identification System (AIS) radio eAtoN. Capabilities unique to virtual eAtoN that are not available using either physical or AIS radio technologies are also examined including dynamic and real time properties and immunity to Global Navigation Satellite System (GNSS) and AIS spoofing, aliasing, denial of service attacks and service outages. Conclusions are provided describing potential methods of deployment based upon similar concepts already in use.

In Strapdown Inertial Navigation System (SINS)/Odometer (OD) integrated navigation systems, OD scale factor errors change with roadways and vehicle loads. In addition, the random noises of gyros and accelerometers tend to vary with time. These factors may cause the Kalman filter to be degraded or even diverge. To address this problem and reduce the computation load, an Adaptive Two-stage Kalman Filter (ATKF) for SINS/OD integrated navigation systems is proposed. In the Two-stage Kalman Filter (TKF), only the innovation in the bias estimator is a white noise sequence with zero-mean while the innovation in the bias-free estimator is not zero-mean. Based on this fact, a novel algorithm for computing adaptive factors is presented. The proposed ATKF is evaluated in a SINS/OD integrated navigation system, and the simulation results show that it is effective in estimating the change of the OD scale factor error and robust to the varying process noises. A real experiment is carried out to further validate the performance of the proposed algorithm.

This critical review addresses 26 studies of Wayfinding conducted in a real-world setting (large-scale), and with subjects using a map or an aerial photograph in their navigation. The purpose of the review is to summarise how previous surveys have been conducted and to provide some methodological advice for future research. Research design has consequently been the main priority, emphasising methods, measures, participants, context and types of map. Both qualitative and quantitative studies should be initiated, but the lack of research is significant within quantitative studies, the link between laboratory-based and real-world studies, studies where the subjects have been children with map experience and adult novices, and studies conducted in complex settings. Type of map appears to be an important determinant of performance and should be varied according to age and level of skills. Alignment of a map could be a possible source of error.

The volume of maritime vessel data, such as available from the Automatic Identification System (AIS), places considerable burden on systems designed and developed to manage data pertaining to maritime traffic. A properly designed and implemented data management infrastructure can provide benefits to the maritime domain awareness research community by supporting data volumes, diverse user needs, and product management. Such an infrastructure has been constructed on a modest budget by utilising open-source technologies. This paper describes the Maritime Situational Awareness Research Infrastructure (MSARI), and the design of the underlying database to meet data volume and user analysis needs. The resulting infrastructure currently handles input rates of approximately two billion vessel reports per month. This work is of potential benefit to those in the navigational community interested in the long-term storage and usage of global vessel data such as that available from AIS.

Accurate navigation systems are required for future pinpoint Mars landing missions. A radio ranging augmented Inertial Measurement Unit (IMU) integrated navigation system concept is considered for the Mars entry navigation. The uncertain system parameters associated with the Three Degree-Of-Freedom (3-DOF) dynamic model, and the measurement systematic errors are considered. In order to improve entry navigation accuracy, this paper presents the Multiple Model Adaptive Rank Estimation (MMARE) filter of radio beacons/IMU integrated navigation system. 3-DOF simulation results show that the performances of the proposed navigation filter method, 70·39 m estimated altitude error and 15·74 m/s estimated velocity error, fulfill the need of future pinpoint Mars landing missions.

This paper describes a statistical method for learning and estimating the risk posed by other craft in the vicinity of a vessel and an overview of its possible spatial application, simulating how professional mariners perceive and assess such risk and using navigational data obtained from a standard integrated bridge. We propose a non-linear model for risk estimation which attempts to capture mariners' judgement. Questionnaire data has been collected that captures and quantifies mariners’ judgements of risk for craft in the vicinity, where each craft is described by measurements that can be obtained easily from the data already present in the ship's navigational equipment. The dataset has then been used for analysis, training and validating Ordered Probit models in order to obtain a computationally efficient data driven model for estimating the risk probability vector posed by other craft. Finally, we discuss how this risk model can be incorporated into decision making and path finding algorithms.

In this paper, a fault-tolerant velocity estimation method is proposed for quadrotors in a GPS denied environment. A novel filter is developed in light of the quadrotor model and measurements from optical flow and inertial sensors. The proposed filter is capable of detecting and isolating the optical flow sensor faults, by which the velocity estimation accuracy and stability will be improved. It is also demonstrated that the wind velocity is observable in the proposed filter. Therefore, the new filter can also be implemented in a windy environment, which is a significant improvement to the previous model-aided inertial sensor estimator. At the end, some simulations are carried out to verify the advantages of our method.

Zero velocity update (ZUPT) is widely discussed for error restriction in land vehicle Inertial Navigation Systems (INSs) and wearable pedestrian INSs to overcome the problems of Global Positioning System (GPS) unavailability in urban canyons or indoor scenarios. In this paper, an online smoothing method for ZUPT-aided INSs is presented. By introducing the Rauch–Tung–Striebel (RTS) smoothing method into the ZUPT-aided INS, position errors can be effectively restrained not only at stop points but during the whole trajectory. By integrating reverse navigation with a ZUPT smoother, the method realises forward and real-time processing. Compared with existing approaches, it can improve the position accuracy in real time without any other sensors, which is well suited for applications on high-accuracy navigation in GPS-challenging environments. Accuracy test results with different Inertial Measurement Units (IMUs) show that the developed method can significantly decrease position errors from hundreds or thousands of metres to below ten metres. During the whole trajectory, the online smoothing method ensures the maximum position errors at non-stop points can reach the same level of accuracy at stop points. A delay test result proves that the delay of the reverse online smoothing method proposed in this paper is much shorter than existing online smoothing methods.

Ship surveillance is important in maritime management. Space-borne Synthetic Aperture Radar (SAR), High Frequency Surface Wave Radar (HFSWR) and the Automatic Identification System (AIS) are three main sensors for the ship surveillance of large maritime areas. Fusion of these sensors' measurements can produce an accurate ship image distribution in a surveillance area. Data association is fundamental to data fusion. A Maximum Likelihood (ML) association algorithm with multi-feature improvements is proposed to increase detection accuracy and reduce false alarms. The tested features are position, size, heading and velocity. First, the ship measurement model is established. Then, the problem of data association for SAR, HFSWR and AIS is formulated as a multi-dimensional assignment problem. In the data assignment process, Jonker-Volgenant-Castanon (JVC) and Lagrangian relaxation algorithms are applied. Simulation results show that the algorithm proposed here can improve the association accuracy compared with the Nearest Neighbour (NN) and the position-only ML algorithms, using the additional features of length and velocity. Real data experiments illustrate that the algorithm can enhance target identification and reduce false alarms.

This study examines maritime routes between ports along the Atlantic coast of the US, utilising Automated Identification System (AIS) data for the years 2010 through 2012. The delineation of vessel routes conducted in this study was motivated by development planned for offshore Wind Energy Areas (WEAs) along the Atlantic coast of the US and the need to evaluate the effect of these development areas on commercial shipping. To this end, available AIS data were processed to generate commercial vessel tracks for individual vessels, though cargo vessels are the focus in this study. The individual vessel tracks were sampled at transects placed along the Atlantic coast. The transect samples were analysed and partitioned by voyages between Atlantic ports to facilitate computation of vessel routes between ports. The route boundary analysis utilised a definition from UK guidance in which routes' boundaries encompassed 95% of the vessel traffic between ports. In addition to delineating route boundaries, we found multi-modal transverse distributions of vessels for well-travelled routes, which indicated preference for lanes of travel within the delineated routes.

Container shipping is one of the most important transport modes in international trade. For a large-scale container shipping network, an optimisation model is proposed to minimise the total shipping cost of container flow. Considering the directional property of the container shipping process, a steering vector search pattern is incorporated in a Max-Min Ant System (MMAS) solving algorithm, and an orthogonal array method is adopted in parameter settings. In numerical experiments, a traditional ant colony algorithm and an MMAS algorithm based on a steering vector search pattern are used respectively to optimise the network of five node scales. The results show that a steering vector search pattern can speed up the computation process and improve the optimisation effect.

The Unscented Kalman Filter (UKF) is a well-known nonlinear state estimation method. It shows superior performance at nonlinear estimation compared to the Extended Kalman Filter (EKF). This paper is devoted to an investigation between UKF and EKF with different feedback control modes in vehicle navigation. Theoretical formulation, simulation and field tests have been carried out to compare the performance of UKF and EKF. The simulation and test results demonstrate that the estimated state of a UKF relies on the measurements and is less sensitive to historical model information. The results also indicate that UKF has benefits for prototype model design due to avoidance of calculation of a Jacobian matrix. EKF, however, is more computationally efficient and more stable.

Procedures based on solutions of the direct and inverse geodetic problems are presented and their application in orthodromic and loxodromic navigation is analysed. They can be used in traditional navigation during a route planning and during sailing on the planned route in real time. The optimal division of orthodromes during planning and locally predicted short loxodromes during sailing – which allow orthodromic navigation also in manual steering – are proposed.

The running fix or sight-run-sight fix is a classic problem in celestial navigation. Methods employed to obtain the fix traditionally involve advancing a Line of Position (LoP) taken at an earlier time and crossing it with one obtained later. Attempts to generalise the operation of advancing an LoP when the Earth's surface is represented by a plane to the case of the sphere have resulted in proposals that contain poorly constrained approximations or are otherwise fundamentally flawed. A simple rapidly-convergent iterative procedure to obtain a running fix is described that avoids the notion of advanced LoPs and is readily applicable to both the sphere and ellipsoid.