The multipath effect and Non-Line-Of-Sight (NLOS) reception of Global Positioning System (GPS) signals both serve to degrade performance, particularly in urban areas. Although receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify direct, multipath-affected and NLOS GPS measurements in order improve ranging-based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (for example, Signal to Noise Ratio (C/N0)) as the classifier. As this single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signals received. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an Adaptive Neuro Fuzzy Inference System (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for LOS, Multipath and NLOS based on a static test with special conditions. These results are superior to the other three state-of-the-art signal reception classification methods.

This paper describes an ionospheric correction technique for single frequency GPS measurements from satellites in low Earth orbit. The fractional total electron content (TEC) above the receiver altitude is obtained from global TEC maps of the International GPS Service network and an altitude dependent scale factor. By choosing a suitable effective height of the residual ionosphere, the resulting path delay for positive elevations is then computed from a thin layer approximation. The scale factor can be predicted from the assumption of a Chapman profile for the altitude variation of the electron density or adjusted as a free parameter in the processing of an extended set of single frequency measurements. The suitability of the proposed model is assessed by comparison with flight data from the Champ satellite that orbits the Earth at an altitude of 450 km. For the given test case, a 90% correction of the ionospheric error is achieved in a reduced dynamic orbit determination based on single frequency C/A-code measurements.

This paper presents key models and concepts developed in psychology which help to understand the role of human factors in complex systems. It aims to explain their significance for anyone who wants to analyze the role of human factors in maritime accidents. Reason (1990, 1997, 2000) makes a crucial distinction between “active failures” that are made by first-line operators (captain, officers or members of a crew) and “latent failures” that are deeper causes and upstream factors concerning people who are at a distance from the accident. In agreement with this theoretical framework, this paper proposes examining the role of human factors in maritime accidents at three different levels: i) the level of individual – and namely cognitive – factors of first-line operators, ii) the level of social – and namely interpersonal – factors and iii) the level of systemic or organizational factors which correspond to “latent failures”. For each level, the main and recent contributions of the human and social sciences are presented and then used to analyze an emblematic accident.

Global navigation satellite system (GNSS) positioning in dense urban areas remains a challenge due to the signal reflection by buildings, namely multipath and non-line-of-sight (NLOS) reception. These effects degrade the performance of low-cost GNSS receivers such as in those smartphones. An effective three-dimensional (3D) mapping aided GNSS positioning method is proposed to correct the NLOS error. Instead of applying ray-tracing simulation, the signal reflection points are detected based on a skyplot with the surrounding building boundaries. The measurements of the direct and reflected signals can thus be simulated and further used to determine the user's position based on the measurement likelihood between real measurements. Verified with real experiments, the proposed algorithm is able to reduce the computational load greatly while maintaining a positioning accuracy within 10 metres of error in dense urban environments, compared with the conventional method of ray-tracing based NLOS corrected positioning.

Attitude estimation systems often use two or more different sensors to increase reliability and accuracy. Although gyroscopes do not have problems like limited range, interference, and line of sight obscuration, they suffer from slow drift. On the other hand, inclinometers are drift-free but they are sensitive to transverse accelerations and have slow dynamics. This paper presents an extended Kalman filter (EKF)-based data fusion algorithm which utilizes the complementary noise profiles of these two types of sensors to extend their limits. To avoid complexities of dynamic modelling of the platform and its interaction with the environment, gyro modelling will be used to implement indirect (error state) form of the Kalman filter. The great advantage of this approach is its independence from the structure of the platform and its applicability to any system with a similar set of sensors. Separate bias formulation of the Kalman filter will be used to reduce the computational complexity of the algorithm. In addition, a systematic approach based on wavelet decomposition will be utilized to estimate noise covariances used in the Kalman filter formulation. This approach solves many of the convergence problems encountered in the implementation of EKF due to the choice of covariance matrices. Experimental implementation of the estimator shows the excellent performance of the filter.

Ship traffic is the factor that presents in almost all of the existing grounding risk models. It is considered to be one of the main factors affecting the expected frequency of ship groundings. This is mostly accepted by experts as common sense. However, there is no research available on the actual dependency between ship traffic and grounding accidents. In this paper, we conduct a study aimed at determining the statistical dependency between the density and distribution of traffic, the number and frequency of grounding accidents and the dependency between the complexity of waterways and an actual accident. For this purpose we utilise statistical analysis of maritime traffic, obtained from Automatic Identification System (AIS) data and grounding accidents, enhanced with the expert elicitation techniques delivering the waterway complexity index. The sea area under investigation is the Gulf of Finland. The results show statistical dependency between frequency of grounding and waterway complexity as well as the traffic distribution. However, the study does not reveal any significant dependency between grounding and traffic density.

First, a brief description is given of an algorithm for the generation of carrier-adjusted differential corrections. Next, the relationship between satellite elevation and the precision of GPS code observations is investigated, on the basis of data collected by TurboRogue SNR-8000, Trimble 4000 SSE and Trimble 4000 SST receivers. It is concluded that the precision of code observations decreases with decreasing elevation, particularly at low elevation and that the relationship can quite well be modelled by an exponential function. For different types of receivers and different types of code observables, this relationship may not be the same.

The Earth's magnetic field has long provided us with a directional reference of almost worldwide usable coverage. This paper examines the use of the magnetic field for ground referenced motion and position measurements

Where E is the vector representation of an electrical field, v vehicle velocity and B a magnetic field, the electromagnetic law of induction, E = v × B, indicates one possibility for measuring ground speed; the magnetic and electrical fields experienced by vehicle mounted sensors being used to solve the equation for v. This method however only gives the component of v perpendicular to the magnetic field. There are also certain difficulties associated with the measurement of B, which should be only the magnetic field of the Earth at the location of the vehicle, and E, which should be only the electrical field resulting from vehicle motion relative to the magnetic field of the Earth. The main problem appears to be the inseparability of motion dependent and non dependent electrical fields, a problem analogous to that of gravitation-acceleration inseparability for inertial navigation systems. The relative magnitudes of the vehicle-motiondependent E-field, of the order of 10−5 (volt/metre)/(metre/second), and the ever-present and very variable non-motion-dependent E-field between a highly conductive atmospheric layer at an altitude of about 50 km and the surface of the Earth, of some 102 volt/metre, are particularly unfavourable. Another potential basis for a ground-speed measurement system is the heterogeneous character of the intensity of the Earth's magnetic field.

Ship collision avoidance involves helping ships find routes that will best enable them to avoid a collision. When more than two ships encounter each other, the procedure becomes more complex since a slight change in course by one ship might affect the future decisions of the other ships. Two distributed algorithms have been developed in response to this problem: Distributed Local Search Algorithm (DLSA) and Distributed Tabu Search Algorithm (DTSA). Their common drawback is that it takes a relatively large number of messages for the ships to coordinate their actions. This could be fatal, especially in cases of emergency, where quick decisions should be made. In this paper, we introduce Distributed Stochastic Search Algorithm (DSSA), which allows each ship to change her intention in a stochastic manner immediately after receiving all of the intentions from the target ships. We also suggest a new cost function that considers both safety and efficiency in these distributed algorithms. We empirically show that DSSA requires many fewer messages for the benchmarks with four and 12 ships, and works properly for real data from the Automatic Identification System (AIS) in the Strait of Dover.

Most previous research has handled the task of ship type recognition by exploring hand-craft ship features, which may fail to distinguish ships with similar visual appearances. This situation motivates us to propose a novel deep learning based ship type recognition framework which we have named coarse-to-fine cascaded convolution neural network (CFCCNN). First, the proposed CFCCNN framework formats the input training ship images and data, and provides trainable input data for the hidden layers of the CFCCNN. Second, the coarse and fine steps are run in a nesting manner to explore discriminative features for different ship types. More specifically, the coarse step is trained in a similar manner to the traditional convolution neural network, while the fine step introduces regularisation mechanisms to extract more intrinsic ship features, and fine tunes parameter settings to obtain better recognition performance. Finally, we evaluate the performance of the CFCCNN model for recognising the most common types of merchant ship (oil tanker, container, LNG tanker, chemical carrier, general cargo, bulk carrier, etc.). The experimental results show that the proposed framework obtains better recognition performance than the conventional methods of ship type recognition.

Thanks to its physical characteristics, Ultra-wideband (UWB) is one of the most promising technologies for indoor pedestrian navigation. UWB radio localisation systems however experience multipath phenomena that decrease the precision and the reliability of the user's location. To cope with complex indoor environments, UWB radio signals are coupled with inertial measurements from Micro Electro Mechanical Sensors (MEMS) in an extended Kalman filter. Improved performances of the filter are presented and compared with reference trajectories and with pure inertial solutions. Only specific selection methods enable this improvement by detecting and removing outliers in the raw localisation data.

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.

The absence of a regional, open water vessel collision risk assessment system endangers maritime traffic and hampers safety management. Most recent studies have analysed the risk of collision for a pair of vessels and propose micro-level risk models. This study proposes a new method that combines density complexity and a multi-vessel collision risk operator for assessing regional vessel collision risk. This regional model considers spatial and temporal features of vessel trajectory in an open water area and assesses multi-vessel near-miss collision risk through danger probabilities and possible consequences of collision risks via four types of possible relative striking positions. Finally, the clustering method of multi-vessel encountering risk, based on the proposed model, is used to identify high-risk collision areas, which allow reliable and accurate analysis to aid implementation of safety measures.

This paper presents the second part of the research activities carried out to develop a novel Global Navigation Satellite System (GNSS) Avionics-Based Integrity Augmentation (ABIA) system for manned and Unmanned Aerial Vehicle (UAV) applications. The ABIA system's architecture was developed to allow real-time avoidance of safety-critical flight conditions and fast recovery of the required navigation performance in case of GNSS data losses. In more detail, our novel ABIA system addresses all four cornerstones of GNSS integrity augmentation in mission- and safety-critical avionics applications: prediction (caution flags), avoidance (optimal flight path guidance), reaction (warning flags) and correction (recovery flight path guidance). Part 1 (Sabatini et al., 2012) presented the ABIA concept, architecture and key mathematical models used to describe GNSS integrity issues in aircraft applications. This second part addresses the ABIA caution and warning integrity flags criteria and presents the results of a simulation case study performed on the TORNADO Interdiction and Strike (IDS) aircraft.

Collision avoidance remains the most important concern for ships at sea. Despite the electronic equipment now fitted on ships to support the mariner, expert experience is still essential when a ship is in danger of colliding with the others. To include these experts' experiences to resolve the problems of collision, we have designed a fuzzy collision-avoidance expert system that includes a knowledge base to store facts and rules, an inference engine to simulate experts' decisions and a fuzzy interface device. Either a quartermaster or an autopilot system can then implement the avoidance action proposed in the research. To perform the task of collision-avoidance effectively, a robust autopilot system using the state space H∞ control methodology has been designed to steer a ship safely for various conditions at sea in performing course keeping, course-changing and route-tracking more robustly. The integration of fuzzy collision-avoidance and H∞autopilot systems is then proposed in this paper.

The paper describes the system developed and used by the United States Hydrographic Office for selecting the optimum track for transoceanic crossings by applying long-range predictions of wind, waves and currents to a knowledge of how the routed vessel reacts to these variables. Over a period of two years, over 1000 optimum ship routes were provided to one authority, with an average reduction in travel time of 14 hours.

This paper introduces an approach for solving a safe ship trajectory planning problem. The algorithm, utilising the concept of a discrete artificial potential field and a path optimisation algorithm, calculates an optimised collision-free trajectory for a ship. The method was validated by simulation tests with the use of real navigational data registered on board the research and training ship Horyzont II. Results of simulation studies demonstrate that the approach is capable of finding a collision-free trajectory in near-real time, and this proves its applicability in commercial collision avoidance systems for ships. The paper contributes to the development of decision support systems for ships and autonomous navigation.

Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM) is essential for safety-of-life and liability critical applications. This paper discusses two fundamentally different ways to assess the integrity risk of an operation with RAIM, based on a different amount of information available: the expected (or average) performance that is computed using the GNSS models only and the real-time (or actual) performance, which also uses information on the internal status of a GNSS receiver. It is shown both theoretically and by simulation that the real-time integrity risk significantly exceeds the expected risk after the detection and exclusion of a failing satellite. Therefore, while most published RAIM algorithms base their performance assessment on the expected performance only, this is only correct when the requirements allow the risk evaluation to be averaged over multiple operations. However, when the GNSS integrity requirement is to be applied on a ‘per operation’ basis, real-time integrity measures are more appropriate.

Course keeping for ships is vital for automatic navigation in marine transportation. To improve the control effect and reduce the energy output of the controller, this article proposes an improved concise backstepping controller based on a Lyapunov candidate function by introducing a nonlinear function of course error to replace the course error itself in the feedback loop. The procedure of nonlinear controller design has been reduced from two steps to one step using information from controlled plant to construct the Lyapunov candidate function. Compared with the pure backstepping control, the proposed improved algorithm reserves the nonlinear item of the system, and possesses a strong disturbance rejection ability and robustness to the mathematical model uncertainty. The algorithm given here has advantages of simplified construction method, satisfactory control effect, robustness and energy saving.

This study aims to evaluate ship collision frequency in port fairways. One case study is created using one month's real-time ship movement data from five major Singapore port fairways. Results show that tankers account for the biggest proportion in the Temasek fairway, whereas the percentage of Roll-on-Roll-Off (RORO) and passenger ships is quite small in the Temasek fairway, Sinki fairway, Jong fairway and Southern fairway. Tankers and container ships are the two major ship types involved in dangerous encounters. The largest number of dangerous head-on and overtaking encounters is located in the Jong fairway. The majority of dangerous crossing encounters have occurred in the West Keppel fairway and Jong fairway. Ship collision frequency at night is found to be significantly higher than during the day in these fairways.