The third generation of the BeiDou navigation satellite system (BDS-3) is a global navigation system, and is expected to be in full operation by 2020. High-precision orbits are a precondition for BDS-3 to provide a highly accurate service, which needs a global tracking and monitoring capability for the operational satellites. However, it is difficult for BDS to construct global ground monitoring stations. Fortunately, Ka-band Inter-Satellite Link (ISL) antennae fitted to the BDS-3 satellites can be used to extend the visible arc of the Medium Earth Orbit (MEO) satellites and to enhance the ground stations for orbit determination. This paper analyses the ISL-enhanced orbit determination for eight BDS-3 satellites, using the data from ten Chinese domestic stations and 13 international Global Navigation Satellite System (GNSS) Monitoring and Assessment System (iGMAS) overseas stations. The results show that the Three-Dimensional (3D) position Root Mean Square (RMS) error of the Overlapping Orbit Differences (OODs) is approximately 1 m when only ten regional stations are used. When the ISL measurements are added, the 3D position RMS error is decreased to 0·5 m, and the accuracy of the 24-hour orbit prediction can also be improved from 2 m to 0·7 m, which is even better than that of the orbits determined using globally distributed stations. It can be expected that with the subsequent launch of BDS-3 satellites and the increasing number of ISLs, the advantage of the ISL enhanced orbit determination will become more significant.

In general, a Reference Station calculates differential corrections which are valid for that exact location (zero baseline) at that particular epoch (age of corrections zero). However, DGPS users may be located as far as 200 nm away from the Reference Station and some of the errors compensated for by the Reference Station vary with space, namely satellite ephemeris, tropospheric and ionospheric errors. Therefore, the corrections calculated at the Reference Station suffer certain accuracy degradation as the separation distance increases, because of a decreasing relevance of the Reference Station data to the user. The error growth with increasing distance to the beacon is accentuated by the inability of Reference Station and user to see the same satellites, commonly termed the lack of intervisibility. The error growth with distance is the most important factor determining DGPS accuracy, but surprisingly very little has been done to assess it. US official documents and IALA state that the achievable accuracy degrades at an approximate rate of 1 m for each 150 km (80 nm) distance from the broadcast site, but this value is based on a theoretical prediction, made back in 1993. To estimate the error growth with real data, 6 DGPS receivers were placed along the Portuguese coastline at approximately 50 nm intervals from Sagres Broadcast Station, in a South – North direction. This paper describes the results of the trial.

Facilitated by recent establishment of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship trajectories are becoming increasingly available and the size of recorded trajectories is getting larger. Large sets of trajectories create problems of storing, transmitting and processing data. Using appropriate methods, an accurate representation of the original trajectories can be obtained by compressing redundant information, while maintaining the main characteristic elements. In this paper, a new scheme and the implementation of the Douglas-Peucker (DP) algorithm are presented, which can simplify AIS trajectories by extracting characteristic points. As for the simplification threshold, the solo parameter of the DP algorithm, a new AIS-based minimum ship domain evaluation method is proposed and acts as criteria for simplification threshold determination. Finally, a validation is made to examine the effectiveness of the DP simplification algorithm and the rationality of the simplification threshold. The result indicates that the DP algorithm can simplify AIS trajectories effectively; the simplification threshold is scientific and reasonable.

Human wayfinding in the natural environment has been practised for thousands of years. In the urban or built environment, few natural cues are available and other aids to wayfinding are needed. This paper considers the problems of wayfinding in airport terminals, what cues are available and how passengers use them to reach their destinations. The use of signs and maps is discussed, and conclusions are drawn on how wayfinding in airport terminals could be improved.

The principles of wayfinding are simple; the practicalities are very complex.

Nainoa Thompson.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.

The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field test data is processed to evaluate the performance of the proposed approach.

The aviation community has very stringent navigation integrity requirements that apply to a variety of manned and Unmanned Aerial Vehicle (UAV) operational tasks. This paper presents the results of the research activities carried out by the Italian Air Force Flight Test Centre (CSV-RSV) in collaboration with the Nottingham Geospatial Institute (NGI) and Cranfield University (CU) in the area of Avionics-Based Integrity Augmentation (ABIA) for mission- and safety-critical Global Navigation Satellite System (GNSS) applications. Based on these activities, suitable models were developed to describe the main causes of GNSS signal outage and degradation in flight, namely: antenna obscuration, multipath, fading due to adverse geometry and Doppler shift. Adopting these models in association with suitable integrity thresholds and guidance algorithms, the ABIA system delivers integrity caution (predictive) and warning (reactive) flags, as well as steering information to the pilot and electronic commands to the aircraft/UAV flight control system. These features allow real-time avoidance of safety-critical flight conditions and fast recovery of the required navigation performance in case of GNSS data losses. This paper presents the key ABIA concepts, architecture and mathematical models. A successive paper will address the ABIA integrity thresholds criteria and detailed results of a TORNADO simulation case-study.

This paper reviews currently existing electronic magnetic sensor technologies for navigation applications. Magnetic compasses have been used in navigation for centuries. The Earth's geomagnetic field is considered to provide accurate, reliable and economically available information for orientation. Meanwhile, modern magnetometers and compass calibration technologies have allowed the electronic compass to become a crucial navigation tool, even in times of modern satellite navigation using Global Navigation Satellite Systems (GNSS). Magnetic sensor technologies, error modelling and compensating approaches have been reviewed in this paper. Current trends and the outlook for future development of the electronic compass are analysed.

During the course of its development for military use and more recent extension to many civilian uses, vulnerabilities of Global Navigation Satellite Systems (GNSS) – in the United States the Global Positioning System (GPS) – have become apparent. The vulnerabilities arise from natural, intentional, and unintentional sources. Increasing civilian and military reliance on GNSS brings with it a vital need to identify the critical vulnerabilities to civilian users, and to develop a plan to mitigate these vulnerabilities. This paper summarizes the findings of the U.S. Department of Transportation (DOT) vulnerability study that addresses these issues. The key findings are that satellite navigation users are vulnerable to several classes of disruption that affect all transportation modes and related infrastructure; but also that the vulnerabilities can be mitigated by awareness, planning, and using independent backup systems and/or alternate procedures in safety-critical applications. To gain the full benefits of GNSS, it will be necessary to analyse safety-of-life vulnerabilities in detail, and to determine the means and costs of reducing these risks to acceptable levels. The complete assessment report, of which this paper is a synopsis, was released to the public on September 10, 2001. Although the basic findings apply to all GNSS, the assessment focused on the GPS, in response to the enabling Presidential Decision Directive.

This paper develops a quantitative model for evaluating the difficulty of ship-handling caused by a restricted manoeuvring area or by traffic congestion or by a combination of both. It includes acceptance criteria based on the mariner's perception of safety. An attempt is made to evaluate the model by applying it to the specific environmental conditions of major ports in Japan. The model can provide information on the degree of ship-handling difficulty and so enable better design of infrastructure for waterways.

In this paper, a novel Voronoi-Visibility (VV) path planning algorithm, which integrates the merits of a Voronoi diagram and a Visibility graph, is proposed for solving the Unmanned Surface Vehicle (USV) path planning problem. The VM (Voronoi shortest path refined by Minimising the number of waypoints) algorithm was applied for performance comparison. The VV and VM algorithms were compared in ten Singapore Strait missions and five Croatian missions. To test the computational time, a high-resolution, large spatial dataset was used. It was demonstrated that the proposed algorithm not only improved the quality of the Voronoi shortest path but also maintained the computational efficiency of the Voronoi diagram in dealing with different geographical scenarios, while also keeping the USV at a configurable clearance distance c from coastlines. Quantitative results were generated by comparing the Voronoi, VM and VV algorithms in 2,000 randomly generated missions using the Singapore dataset.

Course keeping for ships is the core of automatic navigation in sea transportation. Much work has concentrated on developing novel control strategies for closed loop systems. We have turned our attention the other way to improve the control performance of marine autopilots in this work by “modulating” the control error using a sine function while the construction of the controller is not changed. The nonlinear feedback signal thus obtained is sent to the controller to replace the control error itself, which used to be the deviation between the output response and the reference input of the system. Such a control scheme is called “nonlinear feedback control” hereafter. Theoretical analysis by using a describing function and robust control theory shows that the same control quality is guaranteed with minor control actions for the nonlinear feedback scheme. Simulation experiments were carried out for the ship Yulong of Dalian Maritime University. It is shown that the method postulated in this paper has advantages of safety and energy saving in navigation; the maximum initial rudder angle is reduced by 31·2% with satisfactory control effect.

The quality of kinematic navigation and positioning depends on the quality of the model describing the vehicle movements and the reliability of the observations. An adaptive Kalman filtering is introduced. Three kinds of adaptive factors based on the discrepancy between the geometrical positions and the kinematic model predictions and a variance component ratio between model predictions and observations are described. A new exponential adaptive factor is established. The theoretical curves of the adaptive factors are drawn and a practical example is given. The errors of four adaptive filtering results and the corresponding curves of the adaptive factors are also drawn. It is shown, by comparison and analysis, that all of the four adaptive factors can control the influences of the vehicle disturbances in movements on the navigation results. The results derived by the adaptive factor constructed by the variance component ratio are slightly better than those derived by other adaptive factors.

This paper proposes a novel mechanism for the initial alignment of low-cost INS aided by GPS. For low-cost INS, the initial alignment is still a challenging issue because of the high noises from low-cost inertial sensors. In this paper, a two-stage Kalman Filtering mechanism is proposed for the initial alignment of low-cost INS. The first stage is designed for the coarse alignment. To solve the problems encountered by the general coarse alignment approach, an INS error dynamic accounting for unknown initial heading error is developed, and the corresponding observation equation, taking into account the unknown heading error, is also developed. The second stage is designed for the fine alignment, where the classical INS error dynamics based on small attitude error is used. Experimental results indicate that the proposed alignment approach can complete the initial alignment more quickly and more accurately compared with the conventional approach.

Navigation users will significantly benefit from the combined use of GPS and GLONASS due to the improved reliability, availability and accuracy especially in an environment with limited satellite visibility, such as in urban or mountainous areas. But in such situations the visible satellite number is often still insufficient to obtain a position solution even if both GPS and GLONASS measurements are used. This is partly because at least five visible satellites are required to determine a position due to an offset between the timescales of GPS and GLONASS to be solved. In this paper, an algorithm has been proposed to obtain a position solution with only four visible GPS/GLONASS satellites. In addition to the data from IGS stations, an experiment was also conducted to assess the proposed algorithm. The results indicate that using the proposed algorithm with only four GPS/GLONASS satellites a position solution could be obtained at the cost of a slight accuracy loss.

In a previous paper a model was outlined for an encounter between two vessels. This paper shows how the model has been developed to include the simulation of the behaviour of more than two ships, the entrance to harbours, and narrow channels.

In a previous paper by the authors the concept of a domain was examined. Goodwin's definition of a domain was used, namely, ‘the effective area around a ship which a navigator would like to keep clear with respect to other ships and stationary objects’. The concept of distinct sectors for sidelights and sternlight was modified mathematically so that an area equal to the total of the segments was contained within a circle. By off-centering the position of the ship within this circle, the weighting of the differing areas for the various sectors was retained (Fig. I). A second circle with the ship off-centre was introduced called an arena or ‘sphere of influence’. When a ship is inside the arena a navigator becomes aware of the other ship and decides what action, if any, is needed to keep his own domain unviolated. This resulted in a model which obeyed the Collision Regulations.

A navigation integration processing scheme, called the strong tracking unscented Kalman filter (STUKF), is based on the combination of an unscented Kalman filter (UKF) and a strong tracking filter (STF). The UKF employs a set of sigma points by deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. As a type of adaptive filter, the STF is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. In order to resolve the shortcoming in traditional approach for selecting the softening factor through personal experience or computer simulation, a novel scheme called the fuzzy strong tracking unscented Kalman filter (FSTUKF) is presented where the Fuzzy Logic Adaptive System (FLAS) is incorporated for determining the softening factor. The proposed FSTUKF algorithm shows promising results in estimation accuracy when applied to the integrated navigation system design, as compared to the EKF, UKF and STUKF approaches.

The Kalman filter (KF) is a form of optimal estimator characterized by recursive evaluation, which has been widely applied to the navigation sensor fusion. Utilizing the KF requires that all the plant dynamics and noise processes are completely known, and the noise process is zero mean white noise. If the theoretical behaviour of the filter and its actual behaviour do not agree, divergence problems tend to occur. The adaptive algorithm has been one of the approaches to prevent divergence problems in the Kalman filter when precise knowledge on the system models is not available. Two popular types of adaptive Kalman filter are the innovation-based adaptive estimation (IAE) approach and the adaptive fading Kalman filter (AFKF) approach. In this paper, an approach involving the concept of the two methods is presented. The proposed method is a synergy of the IAE and AFKF approaches. The ratio of the actual innovation covariance based on the sampled sequence to the theoretical innovation covariance will be employed for dynamically tuning two filter parameters – fading factors and measurement noise scaling factors. The method has the merits of good computational efficiency and numerical stability. The matrices in the KF loop are able to remain positive definitive. Navigation sensor fusion using the proposed scheme will be demonstrated. Performance of the proposed scheme on the loosely coupled GPS/INS navigation applications will be discussed.

In environments where GNSS is unavailable or not useful for positioning, the use of low cost MEMS-based inertial sensors has paved a way to a more cost effective solution. Of particular interest is a foot mounted pedestrian navigation system, where zero velocity updates (ZUPT) are used with the standard strapdown navigation algorithm in a Kalman filter to restrict the error growth of the low cost inertial sensors. However heading drift still remains despite using ZUPT measurements since the heading error is unobservable. External sensors such as magnetometers are normally used to mitigate this problem, but the reliability of such an approach is questionable because of the existence of magnetic disturbances that are often very difficult to predict. Hence there is a need to eliminate the heading drift problem for such a low cost system without relying on external sensors to give a possible stand-alone low cost inertial navigation system. In this paper, a novel and effective algorithm for generating heading measurements from basic knowledge of the orientation of the building in which the pedestrian is walking is proposed to overcome this problem. The effectiveness of this approach is demonstrated through three field trials using only a forward Kalman filter that can work in real-time without any external sensors. This resulted in position accuracy better than 5 m during a 40 minutes walk, about 0·1% in position error of the total distance. Due to its simplistic algorithm, this simple yet very effective solution is appealing for a promising future autonomous low cost inertial navigation system.

Modern attitude and heading reference systems (AHRS) generally use Kalman filters to integrate gyros with some other augmenting sensors, such as accelerometers and magnetometers, to provide a long term stable orientation solution. The construction of the Kalman filter for the AHRS is flexible, while the general options are the methods based on quaternion, Euler angles, or Euler angle errors. But the quaternion and Euler angle based methods need to model system angular motions, and, meanwhile, all these three methods suffer from nonlinear problems which will increase the system complexities and the computational difficulties. This paper proposes a novel implementation method for the AHRS integrating IMU and magnetometer sensors. In the proposed method, the Kalman filtering is implemented to use the Euler angle errors to express the local level frame (l frame) errors, rather than express the body frame (b frame) errors as the customary methods do. A linear system error model based on the Euler angles errors expressing the l frame errors for the AHRS has been developed and the corresponding system observation model has been derived. This proposed method for AHRS does not need to model system angular motions and also avoids the nonlinear problem which is inherent in the commonly used methods. The experimental results show that the proposed method is a promising alternative for the AHRS.

The maritime domain is the most utilised environment for bulk transportation, making maritime safety and security an important concern. A major aspect of maritime safety and security is maritime situational awareness. To achieve effective maritime situational awareness, recently many efforts have been made in automatic anomalous maritime vessel movement behaviour detection based on movement data provided by the Automatic Identification System (AIS). In this paper we present a review of state-of-the-art automatic anomalous maritime vessel behaviour detection techniques based on AIS movement data. First, we categorise some approaches proposed in the period 2011 to 2016 to automatically detect anomalous maritime vessel behaviour into distinct categories including statistical, machine learning and data mining, and provide an overview of them. Then we discuss some issues related to the proposed approaches and identify the trend in automatic detection of anomalous maritime vessel behaviour.