This study investigates the use of augmented reality technology (AR) in the field of maritime navigation and how researchers and designers have addressed AR data visualisation. The paper presents a systematic review analysing the publication type, the AR device, which information elements are visualised and how, the validation method and technological readiness. Eleven AR maritime solutions identified from scientific papers are studied and discussed in relation to previous navigation tools. It is found that primitive information such as course, compass degrees, boat speed and geographic coordinates continue to be fundamental information to be represented even with AR maritime solutions.

Map matching has been widely applied in car navigation systems as an efficient method to display the location of vehicles on maps. Various map-matching algorithms have been proposed. Inevitably, the correctness of the map matching is closely related to the accuracy of positioning sensors, such as GPS or Dead Reckoning (DR), and the complexity of the road network and map, especially in urban areas where the GPS signal may be constantly blocked by buildings and the road network is complicated. The existing map matching algorithms cannot resolve the positioning problems under all circumstances. They sometimes give the wrong position estimates of the car on road; the result is called mismatching. In order to improve the quality of map matching, a deep understand of the accuracy of sensor errors on mismatching is important. This paper analyses various factors that may affect the quality of map matching based on extensive tests in Hong Kong. Suggestions to improve the success rate of map matching are also provided.

Operational deep-sea manned submersibles are important underwater vehicles that provide strong technical support for international deep-sea research. Navigation and positioning technologies are expected to facilitate the complete exploitation of the scientific value of samples and data collected by such submersibles, improve their underwater operation efficiency and enhance their safety. This paper first briefly describes six operational deep-sea manned submersibles that can dive to depths of more than 4,500 m. Then, the paper focuses on the navigation and positioning technologies used in these submersibles. Finally, considering the existing problems in the navigation and positioning systems of China's manned submersible Jiaolong, the paper discusses future development trends of the navigation and positioning technologies used in operational deep-sea manned submersibles.

Precision inertial navigation depends not only on the quality of the inertial sensors (accelerometers and gyros), but also on the accuracy of the gravity compensation. With a view toward the next-generation inertial navigation systems, based on sensors whose errors contribute as little as a few metres per hour to the navigation error budget, we have analyzed the required quality of gravity compensation to the navigation solution. The investigation considered a standard compensation method using ground data to predict the gravity vector at altitude for aircraft free-inertial navigation. The navigation effects of the compensation errors were examined using gravity data in two gravimetrically distinct areas and a navigation simulator with parameters such as data noise and resolution, supplemental global gravity model noise, and on-track interpolation method. For a typical flight trajectory at 5 km altitude and 300 km/hr aircraft speed, the error in gravity compensation contributes less than 5 m to the position error after one hour of free-inertial navigation if the ground data are gridded with 2 arcmin resolution and are accurate to better than 5 mGal.

Gravity Aided Navigation (GravAN) and Geomagnetism Aided Navigation (GeomAN) are two methods for correcting Inertial Navigation System (INS) errors of Autonomous Underwater Vehicles (AUVs) without compromising the AUV mission. One requirement for applying these methods is the relatively large field feature variations along the navigation trajectory. But in some regions with small gravity or geomagnetic variation, it is very difficult to achieve a reliable result solely by GravAN or GeomAN. If these two methods were combined, gravity and geomagnetism information could be complementary and the aided navigation ability could potentially be improved, especially in those regions when neither method is valid. Based on that concept, a Gravity and Geomagnetism Combined Aided Navigation (GGCAN) method is consequently proposed in this paper as a possible solution. The Gravity Anomaly Grid (GAG2) and Earth Geomagnetic Anomaly Grid (EMAG2) are utilized as the background databases, and then a Multiple Model Adaptive Estimation (MMAE) is adopted to obtain an optimal estimated navigation position. Furthermore, an Optimal Weight Allocation Principle (OWAP) is introduced to the combined GravAN and GeomAN methods, together with a weighted average. In simulation, two special regions in the Western Pacific Ocean were chosen to test the proposed method. The results show that GGCAN can improve the position success rate and reduce the error, compared to GravAN or GeomAN. Results indicate that the GGCAN method proposed in this study is able to improve the accuracy and reliability of an aided navigation system.

Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

A pulse phase estimation of an X-ray pulsar with the aid of vehicle orbital dynamics is proposed. The original continue-time X-ray pulsar signal model is modified to be a term of vehicle position and velocity varying with time, and a modified definition of pulse time of arrival is given. The modified signal model is further linearized around the predicted position and velocity of the vehicle to the second order. The initial phase and the coefficients of the extended signal model can be estimated by maximum likelihood estimator. Some simulations are performed to verify the method and show the method has robustness to the initial error within initial state of the vehicle and is capable of handling the phase-estimation problem for pulsars with low fluxes.

In this paper, we propose a novel dynamic fuzzy ship domain that considers factors associated with both one's own ship and other ships. This is in contrast to existing ship domain models that operate from the perspective of one's own ship, considering only the factors relevant to that ship. First, the domain was determined by considering the distance of the ships around one's own ship in different directions, which sufficiently accounts for factors associated with one's own ship and with the other ships. At the same time, the factors were chosen based on an analysis of their importance. Second, the domain was dynamic and modelled by establishing the relationship between the domain size and the chosen factors in different directions from one's own ship, obtained by using neural networking and wavelet decomposition. Third, the domain was developed using fuzzy sets related to different safety levels and this related the model to the practical applications of estimating spatial collision risk. The model was calibrated using Automatic Identification System (AIS) data of vessel movements in the Bohai Sea and the northern Yellow Sea. The reasonableness and the superiority of establishing a ship domain model considering the factors affecting both one's own ship and other ships were analysed, and the results show that the new model can determine the spatial collision risk of the navigational situation and is thus suitable for use as part of a ship collision avoidance system.

Ultra-wideband (UWB) ranging radios, an emerging technology that offers precise, short distance range measurements are investigated as a method to augment carrier-phase GPS positioning. A commercially available UWB ranging system is used in a tightly-coupled GPS and UWB real-time kinematic (RTK) system. The performance of the tightly-coupled system is evaluated in static and kinematic testing. This work demonstrates that UWB errors can be successfully estimated in a real-time filter. The results of static testing show that the integrated solution provides better accuracy, better ability to resolve integer ambiguities and enhanced fixed ambiguity solution availability compared with GPS alone. In kinematic testing in a degraded GPS environment, sub-decimetre accuracy was maintained.

Autonomous Unmanned Ground Vehicles (UGVs) require a reliable navigation system that works in all environments. However, indoor navigation remains a challenge because the existing satellite-based navigation systems such as the Global Positioning System (GPS) are mostly unavailable indoors. In this paper, a tightly-coupled integrated navigation system that integrates two dimensional (2D) Light Detection and Ranging (LiDAR), Inertial Navigation System (INS), and odometry is introduced. An efficient LiDAR-based line features detection/tracking algorithm is proposed to estimate the relative changes in orientation and displacement of the vehicle. Furthermore, an error model of INS/odometry system is derived. LiDAR-estimated orientation/position changes are fused by an Extended Kalman Filter (EKF) with those predicted by INS/odometry using the developed error model. Errors estimated by EKF are used to correct the position and orientation of the vehicle and to compensate for sensor errors. The proposed system is verified through simulation and real experiment on an UGV equipped with LiDAR, MEMS-based IMU, and encoder. Both simulation and experimental results showed that sensor errors are accurately estimated and the drifts of INS are significantly reduced leading to navigation performance of sub-metre accuracy.

An integrated multi-sensor vehicle navigation system is presented that uses a low-cost rate gyro and differential odometry to supplement GPS under signal masking conditions such as tree foliage and urban canyons. Signal masking is often accompanied by extreme multi-path in urban centres with tall buildings, and is also found in wooded areas, enclosed car parks, tunnels, etc. The purpose of the system tested is to provide an accuracy of better than 20 metres almost 100% of the time throughout these interruptions, which are assumed to last up to a few minutes. The equipment used is discussed in detail, as is the method used for filtering measurements. Results are presented from tests carried out in an urban core with relatively long periods of signal loss – up to several minutes over a 6-km test circuit. Tests in urban canyons demonstrate that it is difficult to reach the above specifications with aiding from differential odometry alone due to the high precision of the wheel-scale factor required. However, with the use of a rate gyro and odometry, RMS errors are below 20 metres while availability is nearly 100%. Some of the large deviations could probably be better controlled if GPS multi-path errors were detected before they are allowed to corrupt the filtered solution.

This paper describes preliminary studies towards a navigational Knowledge-based System currently in progress within the School of Engineering and Technology Management at Liverpool Polytechnic.

Real-time Precise Point Positioning (PPP) has been evolved as a cost-effective technique for highly precise maritime positioning. For a long period, maritime PPP technology has mainly relied on the Global Positioning System (GPS). With the revitalisation of GLONASS and the emerging BeiDou navigation satellite system (BDS), it is now feasible to investigate real-time navigation performance of multi-constellation maritime PPP with GPS, BDS and GLONASS. In this contribution, we focus on maritime PPP performance using real world maritime kinematic data and real-time satellite correction products. The results show that BDS has lower position accuracy and slower convergence time than GPS. The BDS and GPS combination has the best performance among the dual-constellation configurations. Meanwhile, the integration of BDS, GLONASS and GPS significantly improves the position accuracy and the convergence time. Some outliers in the single constellation configuration can be mitigated when multi-constellation observations are utilised.

The Strait of Messina is a very busy sea area that separates Sicily and the Italian mainland. In respect of environment and for the prevention of human loss, it is fundamental to have an estimate of the possible ship accidents that could occur. In this work, the approach used is the International Association of Lighthouse Authorities Waterways Risk Assessment Program (IWRAP) model. The first part of the paper describes the local and global traffic and the separation scheme in the Strait of Messina. The model input data is obtained from the Vessel Traffic Service (VTS) system thanks to the Coast Guard of Messina. The second part concerns calculation of the geometrical collisions (number of collisions in different scenarios) and the causation probability. This analysis is the basis for the discussion of new regulatory constraints due to the future realisation of new piers in the south and the planned unification of the two Port Authorities of the two shores into one single authority.

Navigating in unfamiliar road environments is a common and demanding cognitive activity. If this cannot be accomplished successfully, there are implications for increased driver workload, delays due to navigation errors, potentially unsafe road behaviour such as late lane changes, and inappropriate traffic management. To enable successful navigation through such environments, it is necessary to understand what the navigation task entails, and what a driver's corresponding information requirements are. This paper reports the results of a study that sought to identify what information is used by drivers when navigating within a range of urban driving environments, how this information is used, and what the implications are for the design of navigation aids.

The benefits of integrated INS/GPS systems are well known. However, the knowledge required to jam GPS is becoming public and the hardware to achieve this is basic. When GPS data are unavailable and a low grade INS is used, navigation accuracy quickly degrades to an unacceptable level. The addition of one or more terrain referenced navigation (TRN) systems to an integrated INS/GPS navigation system enables the INS to be calibrated during GPS outages, increasing the robustness of the overall navigation solution. TRN techniques are compared and integration architectures are reviewed. For the initial studies of INS/GPS/TRN integration, radar altimeter based terrain contour navigation (TCN) with a batch processing algorithm is used in conjunction with a centralised integration filter. Four different approaches for using these TCN fixes to calibrate the INS are compared. These are a best fix method, a weighted fix method using a probabilistic data association filter (PDAF) and single and multi-hypothesis versions of the Iterative Gaussian Mixture Approximation of the Posterior (IGMAP) method. Simulation results are presented showing that the single hypothesis IGMAP technique offers the best balance between accuracy, robustness and processing efficiency.

A model is developed for lateral collision risk in air traffic track systems, which resolves the problems of the Reich Model. It is a direct and concrete approach focusing on events, in contrast to Reich's synthetic methodology, in which (e.g.) three types of collision have to be modelled and the focus is on flying hours spent away from the planned flight path. This model makes it straightforward to see what is being assumed, and easy to understand the roles of the main parameters. It is a good starting point for the incorporation of collision detection and the use of hazard analysis.

This paper charts the history of Global Satellite Navigation Systems (GNSS) from the earliest days of the ‘Space Race’ in the 1960s and 1970s to the latest plans for modernisation of existing systems and the development of new systems yet to be deployed or become operational. The paper is based on lectures and presentations given by the author to postgraduate students at The University of Nottingham, students on the RAF General Duties Aero-Systems Course at the Air Warfare Centre, RAF Cranwell and to a number of RIN, RAeS and IEE Branches and to local aviation groups.

Automatic ship reporting systems (AIS – Automatic identification System, LRIT – Long Range Identification and Tracking, VMS – Vessel Monitoring System) today allow global tracking of ships. One way to display the results is in a map of current ship positions over an area of interest, the Maritime Situational Picture (MSP). The MSP is dynamic and must be created by fusing the reporting systems' messages, constructing ship tracks and predicting ship positions to correct for latency especially in the case of AIS received by satellite which forms the bulk of the data. This paper discusses the completeness of the resulting MSP and the accuracy of its positions, quantifying the additional value of the individual data sources.