Mapping global shipping density, including vessel density and traffic density, is important to reveal the distribution of ships and traffic. The Automatic Identification System (AIS) is an automatic reporting system widely installed on ships initially for collision avoidance by reporting their kinematic and identity information continuously. An algorithm was created to account for errors in the data when ship tracks seem to ‘jump’ large distances, an artefact resulting from the use of duplicate identities. The shipping density maps, including the vessel and traffic density maps, as well as AIS receiving frequency maps, were derived based on around 20 billion distinct records during the period from August 2012 to April 2015. Map outputs were created in three different spatial resolutions: 1° latitude by 1° longitude, 10 minutes latitude by 10 minutes longitude, and 1 minute latitude by 1 minute longitude. The results show that it takes only 56 hours to process these records to derive the density maps, 1·7 hours per month on average, including data retrieval, computation and updating of the database.

Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

Swarm Intelligence (SI) constitutes a rapidly growing area of research. At the same time trajectory planning in a dynamic environment still constitutes a very challenging research problem. This paper presents a new approach to path planning in dynamic environments based on Ant Colony Optimisation (ACO). Assumptions, a concise description of the method developed and results of real navigational situations (case studies with comments) are included. The developed solution can be applied in decision support systems on board a ship or in an intelligent Obstacle Detection and Avoidance system, which constitutes a component of Unmanned Surface Vehicle (USV) Navigation, Guidance and Control systems.

In this contribution, a novel un-differenced (UD) (PPP-RTK) concept, i.e. a synthesis of Precise Point Positioning and Network-based Real-Time Kinematic concept, is introduced. In the first step of our PPP-RTK approach, the UD GNSS observations from a regional reference network are processed based upon re-parameterised observation equations, corrections for satellite clocks, phase biases and (interpolated) atmospheric delays are calculated and provided to users. In the second step, these network-based corrections are used at the user site to restore the integer nature of his UD phase ambiguities, which makes rapid and high accuracy user positioning possible. The proposed PPP-RTK approach was tested using two GPS CORS networks with inter-station distances ranging from 60 to 100 km. The first test network is the northern China CORS network and the second is the Australian Perth CORS network. In the test of the first network, a dual-frequency PPP-RTK user receiver was used, while in the test of the second network, a low-cost, single-frequency PPP-RTK user receiver was used. The performance of fast ambiguity resolution and the high accuracy positioning of the PPP-RTK results are demonstrated.

In this paper we present a micro-electrical mechanical system (MEMS) based pedestrian navigation system (PNS) for seamless positioning. The sub-algorithms for the PNS are developed and the positioning performance is enhanced using the modified receding horizon Kalman finite impulse response filter (MRHKF). The PNS consists of a biaxial accelerometer and a biaxial magnetic compass mounted on a shoe. The PNS detects a step using a novel technique during the stance phase and simultaneously calculates walking information. Step length is estimated using a neural network whose inputs are the walking information. The azimuth is calculated using the magnetic compass, the walking information and the tilt compensation algorithm. Using the proposed sub-algorithms, seamless positioning can be accomplished. However, the magnetic compass based azimuth may have an error that varies according to the surrounding magnetic field. In this paper, the varying error is compensated using the MRHKF filter. Finally, the performance enhanced seamless positioning is achieved, and the performance is verified by experiment.

This and the following paper by Mr. Attwooll are the first of a series which will describe the work of the Royal Aircraft Establishment on various aspects of air collision risk and traffic control. Mr. Reich's paper will be presented in three parts, the last two of which deal with the analytical techniques and how they are applied. Mr. Attwooll deals with methods of costing the effects of air traffic control deviations and shows how these costs are related to the size of separation standards. He discusses systems for both subsonic and supersonic transports.

1. Objectives. The main task of air traffic controllers is to plan traffic flows so that aircraft are allotted sufficient separation to absorb not only systematic differences in speed but also the imperfections of navigation and piloting, which we term flying errors. To this end, they usually work with three separation standards, to be applied in, respectively, the along-track, across-track and vertical dimensions of space. The separation standard for any one dimension is the minimum intended (i.e. planned) separation permitted in that dimension when the intended separations are less than standard in the other two. The problem is to choose standards which are safe enough, but not so large as to lead to unnecessary traffic delays and deviations.

Positioning using the Global Positioning System (GPS) is unreliable in dense urban areas with tall buildings and/or narrow streets, known as ‘urban canyons’. This is because the buildings block, reflect or diffract the signals from many of the satellites. This paper investigates the use of 3-Dimensional (3-D) building models to predict satellite visibility. To predict Global Navigation Satellite System (GNSS) performance using 3-D building models, a simulation has been developed. A few optimized methods to improve the efficiency of the simulation for real-time purposes were implemented. Diffraction effects of satellite signals were considered to improve accuracy. The simulation is validated using real-world GPS and GLObal NAvigation Satellite System (GLONASS) observations.

The performance of current and future GNSS in urban canyons is then assessed by simulation using an architectural city model of London with decimetre-level accuracy. GNSS availability, integrity and precision is evaluated over pedestrian and vehicle routes within city canyons using different combinations of GNSS constellations. The results show that using GPS and GLONASS together cannot guarantee 24-hour reliable positioning in urban canyons. However, with the addition of Galileo and Compass, currently under construction, reliable GNSS performance can be obtained at most, but not all, of the locations in the test scenarios. The modelling also demonstrates that GNSS availability is poorer for pedestrians than for vehicles and verifies that cross-street positioning errors are typically larger than along-street due to the geometrical constraints imposed by the buildings. For many applications, this modelling technique could also be used to predict the best route through a city at a given time, or the best time to perform GNSS positioning at a given location.

Global Navigation Satellite System (GNSS) shadow matching is a new positioning technique that determines position by comparing the measured signal availability and strength with predictions made using a three-dimensional (3D) city model. It complements conventional GNSS positioning and can significantly improve cross-street positioning accuracy in dense urban environments. This paper describes how shadow matching has been adapted to work on an Android smartphone and presents the first comprehensive performance assessment of smartphone GNSS shadow matching. Using GPS and GLONASS data recorded at 20 locations within central London, it is shown that shadow matching significantly outperforms conventional GNSS positioning in the cross-street direction. The success rate for obtaining a cross-street position accuracy within 5 m, enabling the correct side of a street to be determined, was 54·50% using shadow matching, compared to 24·77% for the conventional GNSS position. The likely performance of four-constellation shadow matching is predicted, the feasibility of a large-scale implementation of shadow matching is assessed, and some methods for improving performance are proposed. A further contribution is a signal-to-noise ratio analysis of the direct line-of-sight and non-line-of-sight signals received on a smartphone in a dense urban environment.

Versions of this paper were first presented at the Royal Institute of Navigation GNSS Vulnerabilities and Solutions Conference held at Baska, Croatia in September 2008 and the Royal Institute of Navigation NAV 08 Conference held at Church House, Westminster, London in October 2008.

The US Global Positioning System (GPS) is currently the primary source of Position, Navigation and Timing (PNT) information in maritime applications, whether stand-alone or augmented with additional systems. This situation will continue in the future with GPS, possibly together with other Global Navigation Satellite Systems (GNSS) e.g. Galileo, being the core PNT technology for e-Navigation – the future digital maritime architecture. GPS signals, measured at the surface on the Earth, are very weak. As such, the system is vulnerable to unintentional interference and jamming, resulting in possible denial of service over large geographical areas. The result of such interference could be the complete failure of the mariner's GPS receiver or, possibly worse, the presentation to the mariner of hazardously misleading information (HMI) for navigation and situational awareness, depending on how the GPS receiver reacts to the jamming incident. Recognising this, the General Lighthouse Authorities of the United Kingdom and Ireland (GLA), in collaboration with the UK Ministry of Defence (MOD) Defence Science and Technology Laboratory (DSTL), have conducted a series of sea-trials with the aim of identifying the full effects of GPS jamming on safe navigation at sea.

This paper presents the key findings of these trials and provides important information on the effect of GPS denial. The GLA are playing a pivotal role in the establishment of eLoran as an independent source of PNT, taking advantage of eLoran's complementary nature, having dissimilar failure modes to GPS and the future GNSS. This paper provides information on the performance of an eLoran receiver in an area of GPS service denial. The paper presents the rationale for the work, details the system architecture employed, the data gathering efforts and finally the data analysis procedures, results and conclusions.

Within the implementation of the European Geo-stationary Navigation Overlay System (EGNOS), a significant residual error in positioning is due to tropospheric delay effects. The EGNOS guidelines recommend that tropospheric delay is modelled using an empirical correction algorithm based on a receiver's height and estimates of meteorological parameters developed from average and seasonal variation data. However, such a simple average and seasonal variation model is unlikely to emulate temporal weather changes exactly. The potential errors involved in the application of the recommended algorithm and the consequent effects on the positioning errors, under typical UK weather conditions, are detailed in this paper. This was achieved by comparing tropospheric delays produced by the EGNOS model, with tropospheric delays estimated from high precision carrier phase GPS, over a one-year period for five UK stations. The RMS EGNOS model zenith tropospheric delay errors ranged from 4·0 to 4·7 cm, with maximum errors ranging from 13·2 to 17·8 cm. The errors were also shown to be spatially correlated. The subsequent effect on position error is shown to be dependent on the satellite elevation cut-off angle adopted and on whether or not the observations are weighted according to the satellite elevation angle.

This paper aims to estimate Vessel Collision Frequency in the Singapore Strait. This frequency is obtained as the product of the number of Vessel Conflicts and the causation probability using the real-time vessel movement data from the Lloyd's Marine Intelligence Unit (Lloyd's MIU) database. The results show that the container carriers have the highest Vessel Collision Frequency while Roll-On Roll-Off (RORO) and passenger ships have the lowest frequency. Tankers cause the highest head-on collision frequency. In the Singapore Strait, the most risky overtaking area is between longitudes 103°48′E and 104°12′E. The most risky head-on area is between longitudes 103°50′E and 104°00′E while the majority of crossing collisions occur between longitudes 103°50′E and 104°12′E. The Vessel Collision Frequency is found to be 1·75 per year in the traffic lanes. Currently, westbound traffic in the Strait is more risky than eastbound traffic (the number of westbound collisions in July was 0·0991 while the number of eastbound collisions was 0·0470). Furthermore, the estimated Vessel Collision Frequency during the day is less than that at night. The results of this paper could be beneficial for the Maritime and Port Authority of Singapore to further enhance the navigational safety strategies implemented in the Singapore Strait.

Maritime anomaly detection is a key technique in intelligent vessel traffic surveillance systems and implementation of maritime situational awareness. In this paper, we propose a method which combines vessel trajectory clustering and Naïve Bayes classifier to detect anomalous vessel behaviour in the maritime surveillance system. A similarity measurement between vessel trajectories is designed based on the spatial and directional characteristics of Automatic Identification System (AIS) data, then the method of hierarchical and k-medoids clustering are applied to model and learn the typical vessel sailing pattern within harbour waters. The Naïve Bayes classifier of vessel behaviour is built to classify and detect anomalous vessel behaviour. The proposed method has been tested and validated on the vessel trajectories from AIS data within the waters of Xiamen Bay and Chengsanjiao, China. The results indicate that the proposed method is effective and helpful, thus enhancing maritime situational awareness in coastal waters.

In this paper a brief review of Sage adaptive filtering is followed by an analysis of the shortcomings of covariance matrices formed by windowing residual vectors, innovation vectors and correction vectors of the dynamic states. A new adaptive Kalman filter is developed by combining the Sage filter and the variance components and its use tested against various other schemes.

Position calculations, e.g. adding, subtracting, interpolating, and averaging positions, depend on the representation used, both with respect to simplicity of the written code and accuracy of the result. The latitude/longitude representation is widely used, but near the pole singularities, this representation has several complex properties, such as error in latitude leading to error in longitude. Longitude also has a discontinuity at ±180°. These properties may lead to large errors in many standard algorithms. Using an ellipsoidal Earth model also makes latitude/longitude calculations complex or approximate. Other common representations of horizontal position include UTM and local Cartesian ‘flat Earth’ approximations, but these usually only give approximate answers, and are complex to use over larger distances. The normal vector to the Earth ellipsoid (called n-vector) is a non-singular position representation that turns out to be very convenient for practical position calculations. This paper presents this representation, and compares it with other alternatives, showing that n-vector is simpler to use and gives exact answers for all global positions, and all distances, for both ellipsoidal and spherical Earth models. In addition, two functions based on n-vector are presented, that further simplify most practical position calculations, while ensuring full accuracy.

The paper introduces a new measure of collision risk derived from the concept of a ship domain. Its simplicity and the fact that it takes into account the courses of both ships, makes it a good alternative to the commonly used term of the distance at the closest point of approach (DCPA). The measure being presented is flexible enough to be applied in combination with any given ship domain. Derivations of all the necessary equations for the Fuji domain have been presented in detail. Additionally, the paper contains numerical algorithms that are capable of determining value of the measure for any other ship domain. Based on these algorithms a generic method for determination of the necessary course alteration is developed. Also, an example of an already known formula for risk assessment, that may benefit from the measure, is provided. All of the algorithms, formulas and their derivations in the text are presented explicitly, so that they could be directly applied in any collision avoidance or VTS system.

Currently Vessel Traffic Service (VTS) does not have enough technical capability to monitor a crowded surveillance area to maintain safety. Without an efficient alerting system, many marine accidents have occurred due to operator oversight. In this article, a new fuzzy logic method is proposed to add vessel collision avoidance capability to VTS/AIS systems for all potential collision ships in the surveillance area. Starting from the VTS standpoint and integrating AIS data into the Marine Geographic Information System (MGIS) as a platform, the calculations of ship domain and ship inertial force are utilized to generate models of a guarding ring and danger index. By this means, a precise prediction of collision time and position can be achieved using a marine GIS spatial analyst module. The proposed method is able to enhance the VTS operator's decision-making abilities by providing a collision avoidance alerting system.

When an officer of the watch (OOW) faces complicated marine traffic, a suitable decision support tool could be employed in support of collision avoidance decisions, to reduce the burden and greatly improve the safety of marine traffic. Decisions on routes to avoid collisions could also consider economy as well as safety. Through simulating the biological evolution model, this research adopts the genetic algorithm used in artificial intelligence to find a theoretically safety-critical recommendation for the shortest route of collision avoidance from an economic viewpoint, combining the international regulations for preventing collisions at sea (COLREGS) and the safety domain of a ship. Based on this recommendation, an optimal safe avoidance turning angle, navigation restoration time and navigational restoration angle will also be provided. A Geographic Information System (GIS) will be used as the platform for display and operation. In order to achieve advance notice of alerts and due preparation for collision avoidance, a Vessel Traffic Services (VTS) operator and the OOW can use this system as a reference to assess collision avoidance at present location.

The concept of ship's domain plays an important role in navigating a ship. It is a criterion of safety in the process of making decisions by the navigator – in analyzing and assessing a navigational situation and in performing the right manoeuvre. This concept is particularly useful in restricted areas, including narrow fairways, which results from the fact that in such waters a manoeuvring area is restricted so that the criterion of the closest point of approach (CPA) cannot be used. This author has analyzed definitions of ship's domain and methods of its determination found in the literature on the subject. Then a method of navigational safety assessment using the fuzzy domain concept is proposed in reference to a ship in a restricted area. The concept and method of determination have been, respectively, defined and described. The method is based on relevant navigators' knowledge. The methods and tools for knowledge acquisition and representation make use of artificial intelligence. Ships and navigational areas of various size have been considered.

This paper assesses the capability of GPS to provide the level of safety required for different aircraft flight navigation operations. It presents an analysis of the protection offered against potential catastrophic GPS failures at system and user levels. This is followed by an assessment of the different approaches to augmenting GPS for civil air navigation. Results show the inadequacy of GPS as a system for real-time safety critical use.

Conventional visual ship tracking methods employ single and shallow features for the ship tracking task, which may fail when a ship presents a different appearance and shape in maritime surveillance videos. To overcome this difficulty, we propose to employ a multi-view learning algorithm to extract a highly coupled and robust ship descriptor from multiple distinct ship feature sets. First, we explore multiple distinct ship feature sets consisting of a Laplacian-of-Gaussian (LoG) descriptor, a Local Binary Patterns (LBP) descriptor, a Gabor filter, a Histogram of Oriented Gradients (HOG) descriptor and a Canny descriptor, which present geometry structure, texture and contour information, and more. Then, we propose a framework for integrating a multi-view learning algorithm and a sparse representation method to track ships efficiently and effectively. Finally, our framework is evaluated in four typical maritime surveillance scenarios. The experimental results show that the proposed framework outperforms the conventional and typical ship tracking methods.