Despite the extent of works done on modelling port water collisions, not much research effort has been devoted to modelling collisions at port anchorages. This paper aims to fill this important gap in the literature by applying the Navigation Traffic Conflict Technique (NTCT) to measuring the collision potentials in anchorages and for examining the factors contributing to collisions. Building on the principles of the NTCT, a collision potential measurement model and a collision potential prediction model were developed. These models were illustrated by using vessel movement data of the anchorages in Singapore port waters. Results showed that the measured collision potentials are in close agreement with those perceived by harbour pilots. Higher collision potentials were found in anchorages attached to the shoreline and international fairways, but not at those attached to confined water. Higher operating speeds, larger numbers of isolated danger marks and day conditions were associated with reduction in the collision potentials.

This study develops an ordered probit model to evaluate the factors influencing two-ship collision severity using ten years’ ship collision accident data from Fujian sea areas. The model results show that the involvement of big ships has the largest impact in increasing the probability of a serious or very serious accident, followed by the involvement of fishing vessels. There will be a bigger probability of a serious accident if both ships involved in the collision are cargo ships. We found that the season of spring, poor visibility and night time periods are more likely to be factors in high severity levels of ship collision. The results also reveal that lookout failure plays a decisive role in increasing serious accident risk compared with other types of human errors. The results of this study may be beneficial for policy-makers in proposing efficient strategies to reduce the likelihood of serious ship collisions.

The modelling of most Global Navigation Satellite System (GNSS) errors/biases and developments of data processing techniques have been improved substantially since the birth of Global Positioning System (GPS), however, there has been much less progress in the improvement of phase multipath mitigation techniques. Multipath therefore remains one of the most important error sources in high precision GNSS positioning. This is because multipath is site-dependent and therefore cannot be eliminated by differencing techniques. Also multipath is highly dependent on satellite-reflector-antenna geometry, which usually causes rapid changes in phase multipath errors especially in Real-Time Kinematic (RTK) applications. Multipath mitigation for static antennas such as those at reference stations can be carried out by site calibration, averaging over long observation times, and through the estimation of the error using filtering based on signal-to-noise ratio (SNR) data. However, multipath mitigation for kinematic antennas is still very difficult today.

Nevertheless, much research has been carried out on a particular class of phase multipath mitigation techniques: ones that can be applied within positioning algorithms (rather than incorporated into the receiver tracking loops or antennas). This paper investigates and further develops a number of state-of-the-art techniques in this category. They include phase multipath estimation using SNR data, phase multipath estimation through the use of closely spaced antennas, multipath mitigation stochastic models such as the satellite elevation angle model and SNR-based models (SIGMA-∊ model and our modified SNR-based model), and our own novel ray-tracing method. The techniques are tested with both real and simulated data, the real test datasets have been collected on the Laboratoire Central des Ponts et Chaussées (LCPC) testbed near Nantes in France, and on the campus of the University of Nottingham during SPACE data collection experiments.

The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.

Interoperability of GNSS Open Services is a significant trend in the development of all the satellite navigation systems, and its performance will directly affect the users' security and reliability. Therefore, the monitoring and assessment of GNSS Open Services has become a focus of attention for all providers and users of GNSS. In this paper the elements and methods for the monitoring and assessment of GNSS Open Services are investigated first. Then the schemes designed for an international GNSS Monitoring and Assessment System (iGMAS) are proposed. The schemes are based on omni-directional antennas, multi-beam antennas and a high-gain paraboloid antenna to achieve Quadruple-overlap and Single-overlap coverage and sophisticated analysis respectively. Moreover, the scheme for the deployment of world-wide monitoring stations is also proposed. Finally, some related works that have been conducted to monitor and assess Beidou Open Services are introduced, which verifies the feasibility of the proposed system.

Autonomous vehicles with an ability to trace chemical plumes can be instrumental in tasks such as detection of unexploded ordnance, search for undersea wreckage and environmental monitoring. As a consequence, use of autonomous vehicles to perform chemical plume tracing has received an increasing interest from the research community in recent years. Owing to the diversity of applications and ambient fluid environment of the plumes, there are numerous plume tracing strategies and approaches. This paper reviews two main approaches and a number of strategies that have been successfully implemented to track air or water borne plumes in order to locate odour sources using autonomous vehicles. The first strategy considered is the biomimetic approach that offers excellent models for the development of robotic systems. Strategies inspired by lobsters and bacterium are the main focus in this study. The second scheme considers parallelization of the search procedure by employing a multi-robot approach. This approach has the advantage of utilising a group of smaller and simpler communicating robots which are capable of performing a collaborative search of the plume.

For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.

Increased fuel costs necessitate a careful consideration of consumption when planning voyages trans-ocean. A model is formulated whereby ship response algorithms are matched with forecast environmental conditions expected for the duration of a voyage. A discrete system allows an optimization routine to minimize fuel costs for a trans-atlantic passage, using dynamic programming techniques.

These are simulated by using an on-board boosted micro-computer. The results are discussed and limitations of the system considered.

The performance of Global Navigation Satellite System (GNSS) and Micro-Electro-Mechanical System (MEMS)-based Inertial Navigation System (INS) integrated navigation is reduced during GNSS outages. To bridge the period during GNSS outages, a novel hybrid intelligent algorithm incorporating a Discrete Grey Predictor (DGP) and a Multilayer Perceptron (MLP) neural network (DGP-MLP) is proposed. The DGP-MLP is used to provide a pseudo-GNSS position to correct the INS errors during GNSS outages; the DGP uses the GNSS position information of the latest few moments to predict the position of future moments; in the process of DGP-MLP, the MLP is used to modify the prediction errors of DGP, and the MLP is improved by adding momentum terms and adaptively adjusting the learning rate and momentum factor. To evaluate the effectiveness of the proposed methodology, four GNSS outages in different cases over a real field test data were employed. The experimental results demonstrate that the proposed methodology can significantly improve positioning accuracy during GNSS outages.

Two main concepts in mathematical modelling of ship encounters have been proposed by Davis. The first, the ‘domain’, was an adaptation of a concept originally introduced by Goodwin, who defined the domain as the ‘area about own-ship that a navigator wished to keep free with respect to ships and other stationary objects’. The second, the ‘arena’, conceived by Davis, is the area around the domain which when infringed causes the mariner to consider whether to make a collision-avoidance manoeuvre. Thus, in a computer model, when a vessel enters the arena the computer analyses the situation and, depending on the severity of the threat, makes a collision-avoidance manoeuvre.

Goodwin's domain was divided into three sectors corresponding to the ‘giveway’, the ‘stand-on’ and the ‘overtaking’ regions as defined by the relative velocity of approach. The domain was derived from radar films of ship tracks and records of radar simulator experiments. Davis smoothed the sectored domain to a circle with own-ship off-centred astern and to port, and the weighting of each of the sectors retained. Davis's domain had a solid theoretical grounding; the arena, however, was simply a larger version of the smoothed domain. Its size and position were obtained by means of a well-distributed questionnaire. It served its purpose in the model, but lacked any real validation.

One problem with the Davis arena was its inability automatically to take into account different velocities, both of own-ship and of targets.

This study develops a Zero-Inflated Negative Binomial (ZINB) regression model to evaluate the factors influencing the loss of human life in shipping accidents using ten years' ship accident data in the South China Sea. The ZINB regression model results show that the expected loss of human life is higher for collision, fire/explosion, contact, grounding, hull damage, machinery damage/failure and capsizing accidents occurring in adverse weather conditions during night periods. Sinking can cause the highest loss of life compared to all other accident types. There are fewer fatalities and missing people when the ship involved in an accident is moored or docked. The results also reveal that the loss of human life is associated with shipping accidents occurring far away from the coastal area/harbour/ports. The results of this study are beneficial for policy-makers in proposing efficient strategies to reduce shipping accident casualties in the South China Sea.

A version of this paper was first presented at the European Navigation Conference 2006, held under the auspices of the RIN at Manchester on 8–10 May 2006.

AIS is the most recent example of a major globally-introduced maritime navigation system. During its evolution, introduction and early in-service life, it has aroused a lot of negative comment. The basis for this negativity is examined, particularly in the light of the real experience that is now being gained by users of the system. The evolution of the AIS concept through the relevant international bodies – IMO, ITU, IALA and IEC – is discussed, together with the type of problems encountered during its introduction. Many of the problems would have been ameliorated if there had been better communication to users of the system. It is argued that the complexity of the system really required mandatory training to be undertaken, in parallel with the installation of the new equipment.

The aim of the Integrated Navigation System (INS) on a ship bridge should be to provide the navigator with added value and aid in the complex task of conducting a safe and efficient passage at high speeds in demanding waters. This article presents a method for analysing eye tracking data to reveal sub-optimal design in the bridge layout and in the software graphical user interface on a maritime navigation display. The analysis of eye tracking data with a focus on scan path events indicates sub-optimal design, and the paper provides suggestions for improvement in design and interfaces. Pros and cons of using Eye Tracking Glasses in a maritime environment are presented. The importance of not affecting the normal behaviour of the navigator by collecting data is stressed, and how the software should provide good visualisation and interpretation of the eye tracking data.

This paper deals with the modelling of airspace capacity in Europe by first considering the factors that affect controller workload and then using a model, aided by the appropriate analytical techniques, to make an estimate of airspace capacity. The results show that capacity can be estimated based on the combination of different types of air traffic movement in a sector. The model has been used to provide airspace capacity estimates and utilisation measures for ATC sectors in Europe.

Recent navigation systems integrating GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Measuring Units (IMUs) have shown promising results for several applications based on low-cost devices such as vehicular and personal navigation. However, as a trend in the navigation market, some applications require further reductions in size and cost. To meet such requirements, a MEMS full IMU configuration (three gyros and three accelerometers) may be simplified. In this context, different partial IMU configurations such as one gyro plus three accelerometers or one gyro plus two accelerometers could be investigated. The main challenge in this case is to develop a specific navigation algorithm for each configuration since this is a time-consuming and costly task. In this paper, a universal approach for processing any MEMS sensor configuration for land vehicular navigation is introduced. The proposed method is based on the assumption that the omitted sensors provide relatively less navigation information and hence, their output can be replaced by pseudo constant signals plus noise. Using standard IMU/GPS navigation algorithms, signals from existing sensors and pseudo signals for the omitted sensors are processed as a full IMU. The proposed approach is tested using land-vehicle MEMS/GPS data and implemented with different sensor configurations. Compared to the full IMU case, the results indicate the differences are within the expected levels and that the accuracy obtained meets the requirements of several land-vehicle applications.

Transfer Alignment (TA) is the initialisation process of the Inertial Navigation System (INS) of an air-launched weapon before its release by using the data from the aircraft INS. The aim of this paper is to improve the TA performance by taking into account the aircraft flexures deterministically. The developed procedure neither requires captive carry tests for determination of flexures nor increases the size of the problem, and can be used in real-time missions of any type of military aircraft. The procedure is evaluated for the Velocity Match (VM) method as well as the Velocity and Attitude Match (VAM) method, which are applied through a Kalman Filter (KF). Using a short-time Wing-Rock (WR) manoeuvre, the results of both methods are compared to each other for two cases in which either the flexures are taken into account deterministically, or modelled as noise by assuming that they are unknown. Standard deviations of the errors and the Circular Error Probable (CEP) variations have shown that the TA performance of the VAM method can be much improved if aircraft flexures are incorporated deterministically into the method. The improved performance makes possible target of opportunity missions at shorter weapon ranges, and it decreases target strike errors.

Micro-Electro-Mechanical System (MEMS)-based inertial technology has recently evolved. It holds remarkable potential as the future technology for various navigation related applications. This is mainly due to the significant reduction in size, cost, and weight of MEMS sensors. A major drawback of low-cost MEMS-based inertial sensors, however, is that their output signals are contaminated by high-level noise. Unless the high frequency noise component is suppressed, optimizing the pre-filtering methodology cannot be achieved. This paper proposes a neural network-based de-noising model for MEMS-based inertial data. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Simulated and real MEMS-based inertial data sets are used to validate the model. It is shown that the model is capable of reducing the noise of the Crossbow's AHRS300CA IMU data by over one order of magnitude without altering the stochastic nature of the original signal. This is of utmost importance in developing a generic stochastic model for MEMS-based inertial data. A comparison between the developed neural network model and the wavelet de-noising method is made to further validate the model. It is shown that achieving the same level of noise suppression with wavelet-based de-noising model changes the stochastic characteristics of original signal.

Future air traffic management will require a variety of automated decision support tools to provide conflict-free trajectories and their associated error margins. The ability to correctly forecast aircraft trajectories, i.e. trajectory prediction, is the central component of such automated tools, which will enable continued provision of safe and efficient services in increasingly congested skies. Current approaches for trajectory prediction, available in the open literature, make a number of assumptions in order to simplify the mathematical models of aircraft motion. Furthermore, many existing methods perform three-dimensional trajectory prediction, in which information on expected times of arrival at significant points along the intended aircraft route is not considered. This results in inaccurate trajectories not suitable for conflict detection and resolution. This paper presents a novel four-dimensional trajectory prediction scheme that makes full use of data on expected times of arrival. A three dimensional point-mass model for a standard civil aircraft is used to emulate aircraft dynamics, while the aircraft operating mode is characterised through a set of discrete variables. The aircraft performance model used relies on the EUROCONTROL Base of Aircraft Data (BADA) set and the computed trajectory accounts for the effects of wind. Inputs include navigation data and aircraft intent information, which unambiguously define the trajectory to be computed according to the flight plan. In the proposed model, aircraft intent information is summarised in a simple, but effective, set of instructions contained in a Flight Script. Furthermore, two key innovations to trajectory prediction are introduced. Firstly, a novel scheme to emulate the control system used for aircraft lateral guidance is proposed and secondly, on the basis of aircraft intent information, a new procedure to estimate speed is presented. The performance of the enhanced trajectory model proposed is quantified using a detailed operational dataset (real flight data) captured in a European airspace. The results show that, over an extended time-horizon, the enhanced model is more accurate than two representative existing methods, and that it is suitable for reliable trajectory prediction.

This paper describes the United States Coast Guard's efforts regarding Long Range Identification & Tracking (LRIT) of ships. Among those elements included in the LRIT plan, automatic identification systems (AIS), typically envisioned as a short-range system, may also play a long-range role. AIS vessel tracking may be accomplished through high elevation shore sites allowing reception of AIS signals to nominally 24 nm that loosely puts it in the category of a short-range sensor. Additionally, the US Coast Guard is conducting research and development efforts to determine the feasibility of using AIS receive capability on low earth orbit (LEO) satellites and high altitude, long endurance (HALE) airships or balloons. In parallel with the developing technical capabilities, the US approach with the International Maritime Organization (IMO), and domestic legislation and regulations are also addressed. Finally, a vision for the implementation of LRIT is offered. The paper was presented on 9 November 2004 at Church House, London during the Institute's NAV 04 Conference.

In order to enhance the independent viability of high-orbit satellites, an X-ray pulsar-based navigation (XNAV)/celestial navigation system (CNS) integrated navigation method is proposed. An improved kinematic and static filter is derived to fulfil data fusion that can obtain an optimal estimation for global use. In the filter, unscented transformation is used to reduce linearization error, and the technique of separate-bias is used to reduce the impacts of systematic errors in XNAV measurements. The results of simulations have shown that the proposed navigation system can reach a positioning accuracy of less than 100 m, an improved performance over separate XNAV and CNS.