In dense urban areas, conventional Global Navigation Satellite Systems (GNSS) positioning can exhibit errors of tens of metres due to the obstruction and reflection of the signals by the surrounding buildings. By using Three-Dimensional (3D) mapping of the buildings, the accuracy can be significantly improved. This paper demonstrates the first integration of GNSS shadow matching with 3D-mapping-aided GNSS ranging. The integration is performed in the position domain, whereby separate ranging and shadow matching position solutions are computed, then combined using direction-dependent weighting. Two weighting strategies are compared, one based on the computation of ranging-based and shadow matching position error covariance matrices, and a deterministic approach based on the street azimuth. Using experimental data collected from a u-blox GNSS receiver, it is shown that both integrated position solutions are significantly more accurate than either shadow matching or 3D-mapping-aided ranging on their own. The overall Root Mean Square (RMS) horizontal accuracy obtained using covariance-based weighting was 6·1 m, a factor of four improvement on the 25·9 m obtained using conventional GNSS positioning. Results are also presented using smartphone data, where shadow matching is integrated with conventional GNSS positioning.

This paper concentrates on designing an autonomous navigation scheme for Mars exploration. In this scheme, formation flying spacecraft are used to realise absolute orbit determination when orbiting around Mars. Inertial Line-Of-Sight (LOS) vectors from “deputy” spacecraft to the “chief” are measured using radio cross-link, optical devices and attitude sensors. Since the system's observability is closely related to the navigation performance, an analytical approach is proposed to optimise the observability. In this method, the gravity gradient tensor difference is chosen as the performance index to optimise two navigation scenarios. When there is one deputy flying around the chief, optimal parameters are obtained by solving the constrained optimisation problem. When a second deputy is added into the formation, the optimal configuration is also obtained. These results reveal that the observability is mainly determined by the magnitude of the in-track and cross-track distances in the configuration. An Extended Kalman Filter (EKF) is used to estimate the position and velocity of the chief. The results of a navigation simulation confirms that adding more deputies can significantly improve the navigational performance.

In this paper we develop the nonlinear motion equations in terms of the true anomaly varying Tschauner–Hempel equations relative to a notional orbiting particle in a Keplerian orbit, relatively close to an orbiting primary satellite to estimate the position of a spacecraft. A second orbiting body in Earth orbit relatively close to the first is similarly modelled. The dynamic relative motion models of the satellite and the second orbiting body, both of which are modelled in terms of independent relative motion equations, include several perturbing effects, such as the asymmetry of the Earth gravitational field resulting in the Earth's oblateness effect and the third body accelerations due to the Moon and the Sun. Linear control laws are synthesised for the primary satellite using the transition matrix so it can rendezvous with the second orbiting body. The control laws are then implemented using the state estimates obtained earlier to validate the feedback controller. Thus, we demonstrate the application of a Linear Quadratic Nonlinear Gaussian (LQNG) design methodology to the satellite rendezvous control design problem and validate it.

The Right Whale Sighting Advisory System (RWSAS) is a National Oceanic and Atmospheric Administration (NOAA) Fisheries program designed to reduce collisions between vessels and critically endangered North Atlantic right whales. The vessel speed restriction that is part of the RWSAS presents navigation stakeholders with numerous challenges, owing to concerns about increased risks of ship grounding and collisions within ports. In this paper, we present a multi-methodology framework for assessing the impact of the vessel speed restriction on navigational safety. Empirically, we base our discussion in a first-order analysis of ship grounding risk for the Charleston Entrance Channel. Our analysis proceeds in three parts. We begin by using fault and event tree analyses to assess a relevant set of grounding-related event progression and failure probabilities. The influence of alternative vessel speed restrictions on ship grounding risk are then explored via a Bayesian network model that utilises the previously specified fault and event tree models for its partial specification and enumeration. Our analysis suggests that the speed restriction can, under certain reasonable assumptions, be seen to adversely impact the risk of ship grounding accidents in the Charleston Entrance Channel. We conclude with a summary of our findings and recommendations for future research.

Tight integration of inertial sensors and stereo visual odometry to bridge Global Navigation Satellite System (GNSS) signal outages in challenging environments has drawn increasing attention. However, the details of how feature pixel coordinates from visual odometry can be directly used to limit the quick drift of inertial sensors in a tight integration implementation have rarely been provided in previous works. For instance, a key challenge in tight integration of inertial and stereo visual datasets is how to correct inertial sensor errors using the pixel measurements from visual odometry, however this has not been clearly demonstrated in existing literature. As a result, this would also affect the proper implementation of the integration algorithms and their performance assessment. This work develops and implements the tight integration of an Inertial Measurement Unit (IMU) and stereo cameras in a local-level frame. The results of the integrated solutions are also provided and analysed. Land vehicle testing results show that not only the position accuracy is improved, but also better azimuth and velocity estimation can be achieved, when compared to stand-alone INS or stereo visual odometry solutions.

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

The current Korean national geodetic reference frame, KGD2002, refers to the fixed epoch at 2002·0 under the assumption that there is no crustal movement of the Korean peninsula. A discontinuity in the coordinates of the reference stations may occur due to the relocation of the stations, antenna replacement, or earthquakes. The static reference frame has difficulty in covering continuous and/or discontinuous crustal movements at the same time. A new dynamic local geodetic reference frame has been calculated based on eight years (2007–2014) of Global Navigation Satellite System (GNSS) data. The final geodetic coordinates and velocities were calculated on the basis of the IGb08 reference frame. The discontinuity caused by the 2011 Tohoku earthquake can be addressed using the newly proposed model in this study, which ensures the consistency and continuity of the local geodetic datum.

Global Navigation Satellite System (GNSS) attitude determination and positioning play an important role in many navigation applications. However, the two GNSS-based problems are usually treated separately. This ignores the constraint information of the GNSS antenna array and the accuracy is limited. To improve the performance of navigation, an integrated attitude and position determination method based on an affine constraint model is presented. In the first part, the GNSS array model and affine constrained attitude determination method are compared with the unconstrained methods. Then the integrated attitude and position determination method is presented. The performance of the proposed method is tested with a series of static data and dynamic experimental GNSS data. The results show that the proposed method can improve the success rate of ambiguity resolution to further improve the accuracy of attitude determination and relative positioning compared to the unconstrained methods.

This paper optimises two-ship collision-avoidance manoeuvres accounting for both collision risk and fuel use. A collision-avoidance manoeuvring optimisation model is developed to minimise fuel consumption while assuring ships' operational safety. The model can optimally determine when to begin collision-avoidance actions, how to change courses, and what rudder angles are needed. A quantitative scenario simulation is developed to illustrate the model application. The methodology can be further developed to guide practical ship collision-avoidance manoeuvring decisions made under more operational scenarios. In particular, this research can contribute to the development of computer-aided collision-avoidance operations to improve the safety and energy efficiency of maritime transportation.

The importance of the Global Positioning System (GPS) and related electronic systems continues to increase in a range of environmental, engineering and navigation applications. However, civilian GPS signals are vulnerable to Radio Frequency (RF) interference. Spoofing is an intentional intervention that aims to force a GPS receiver to acquire and track invalid navigation data. Analysis of spoofing and authentic signal patterns represents the differences as phase, energy and imaginary components of the signal. In this paper, early-late phase, delta, and signal level as the three main features are extracted from the correlation output of the tracking loop. Using these features, spoofing detection can be performed by exploiting conventional machine learning algorithms such as K-Nearest Neighbourhood (KNN) and naive Bayesian classifier. A Neural Network (NN) as a learning machine is a modern computational method for collecting the required knowledge and predicting the output values in complicated systems. This paper presents a new approach for GPS spoofing detection based on multi-layer NN whose inputs are indices of features. Simulation results on a software GPS receiver showed adequate detection accuracy was obtained from NN with a short detection time.

Unlike industrial-grade Inertial Navigation Sensors (INSs) that can provide credible tracking performance, more affordable consumer-grade low-cost INSs produce drifts in heading angles and positions that result in a poor tracking accuracy. Researchers have proposed drift correction methods that attempt to attenuate the drifts when walking straight along the dominant directions is detected. While determining the type of a pedestrian's walk is essential before the heading corrections are made, the current detection techniques heavily rely on thresholding. This paper proposes a novel threshold-less method to detect turns in walking by using pelvic rotation and correct the heading angle based on consumer-grade INSs. The experiments indicate the proposed turn detector and heading correction methods produce very good results which can be applied for future pedestrian tracking, activity recognition or rehabilitation.

For underwater vehicle navigation sensors, the output signal periods are different and time-varying. This would result in the decline of precision, and even wrong results. To deal with the problem, this paper puts forward a multi-sensor, adjustable-period integrated navigation method based on multi-stage signal trigger. This method considers the valid signals of a Doppler Velocity Log (DVL) as the trigger signals of a Dead-Reckoning (DR) navigation program. It also considers the valid signals of an acoustic positioning sensor as the trigger signals of the integrated navigation program. In this method, it can adjust the filtering period in real time. According to the time label of signals, this method actualises the time-space alignment of sensors. Then it conducts DR navigation and integrated navigation. The method can not only utilise the valid signals of each sensor sufficiently but also fuses the data based on time-space alignment efficiently. Sea trial data shows that when the output signal periods are certain, navigation precision of the method in this paper is better than a non-adjustable-period filtering method. Moreover, in poor conditions, it can also attain a high precision.

Approaching the problem from the internal factors and in particular the inherent state model of a Kalman Filter, this paper presents a novel Strapdown Inertial Navigation System (SINS) modelling, which is obtained with a pseudo-north-oriented mechanisation in a pseudo-geographic frame. Improved modelling associated with the backward algorithm is proposed to achieve velocity-aided in-motion alignment. Compared with traditional algorithms, the proposed method can eliminate the influence of alignment model on the performance of initial alignment caused by SINS modelling. On the other hand, the backward process can still be used to accelerate the process of alignment. As a result, the proposed method is expected to assist those methods only considered from external factors (such as coarse accuracy, process noise, measurement noise, and so on) to improve the stability and robustness of a velocity-aided in-motion alignment system and to solve the modelling problem of high latitude alignment without sacrificing alignment accuracy. Finally, simulations and field experiments with a navigation-grade SINS demonstrate the superior performance of the proposed method.

The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.