The Micro-Nano satellite TianTuo-3 (TT-3) developed by the National University of Defense Technology (NUDT) was successfully launched on 20 September 2015. The space-based Automatic Identification System (AIS) on board TT-3 works well and stably receives AIS signals from global vessels. In this work, we perform statistical analysis on the detection probability of the vessels in the concerned areas by using the TT-3 AIS data. The results suggest that the detection probability of vessels decreases as the distribution density increases, especially in the offshore areas of dense traffic and the TT3-AIS vessel detection probability in the oceans can be higher than 40%, indicating that the TT-3 AIS has achieved a high probability of coverage of vessels for a single receiving antenna. The analysis results will present helpful references both in evaluating the potential application of satellite-based AIS and for designing the next generation space-based AIS which might greatly improve the detection probability of ocean-going vessels.

Marine accident analysis is important for ships passing through narrow, shallow and busy waterways. This study analyses the accidents which have occurred in the Istanbul Strait and proposes both quantitative and qualitative assessments of marine accidents. Marine accidents occurring in the Istanbul Strait are analysed by using a method based on neuro-fuzzy and genetically optimised fuzzy classifiers. It can be concluded that accident severity increases when poor weather conditions prevail in the Strait regardless of ship size. Therefore, solutions to reduce unwanted events should be prioritised by accounting for weather conditions and the capacity of the vessels. This analysis indicates that the safety level would be significantly improved if all the vessels follow the passage guidelines.

We examine the relation between fix error and the azimuthal distribution of lines of position by subsuming the distribution into a scalar form Q analogous to the nematic order parameter of statistical physics. It is shown in the least-squares approach that the error behaves as (1−Q2)−1/2. Simulation results are presented to demonstrate the control of Q as a strategy for fix improvement.

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.

This paper describes George Biddell Airy's almost completely unknown method of approximating an orthodromic arc (great circle arc) using a circular arc in the normal aspect Mercator projection of a sphere. In addition, it is demonstrated that the centre of the circle can be defined in at least two different ways, which yields slightly different results. Airy's approach is built upon in this paper. The method of computing coordinates of Airy's circle arc centre is described. The formulae derived in the paper can be used to calculate the length of Airy's approximation of the orthodromic arc connecting two points on the sphere and on the Mercator chart. Moreover, the actual length of the orthodromic arc on the sphere and on the Mercator chart can be computed using the formulae derived in this paper. The purpose of the paper is not to suggest an application of Airy's method in navigation, but to analyse Airy's proposal and to show that a great circle arc on a Mercator chart is close to a circular arc for distances which are not too great. This property can be useful in education, having in mind that the stereographic projection is the only one that maps any circle on a sphere onto a circle in the projection plane.

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.

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.

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.

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.

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.

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.

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.

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.

The Strait of Istanbul is one of the most congested and risky waterways in the world. Navigation patterns have been investigated using Automatic Indentification System (AIS) data collected over a long period. 1·5 billion AIS messages, gathered over a year from 309,000 moving vessels in the Strait were stored in a Structured Query Language (SQL) database. Grid-based analysis is used to track the time, number, position, type, dimension, heading, speed and course over ground of ships. Local traffic, whose effect on maritime risk has often been neglected, is found to dominate transit traffic by a ratio of eight to one. Vessel distributions indicate that the most common lengths of vessels are 100 m and 170 m. Draught analysis shows a net transfer of goods from north to south. Southbound vessels are more likely to exceed the enforced speed limit due to predominant currents. Courses indicate that the local traffic strongly affects navigation patterns, especially at sectors with sharp turns. All these results help to understand the navigation patterns of ships and give the necessary input to assist in predicting maritime risk.

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.

The major challenge of current Strapdown Inertial Naviagtion System/Celestial Navigation System (SINS/CNS) integrated systems is navigation accuracy degradation due to the failure to accurately estimate the accelerometer bias even when using stellar refraction information (e.g. the number of refraction stars is less than three). To solve this problem, this paper presents a new method for improving the accuracy of the traditional inertial-based integrated systems installed on ballistic vehicles. In an analogy with nonholonomic constraints in land navigation, this algorithm exploits the constraints that govern the motion of a ballistic vehicle in the free flight phase to obtain accelerometer bias observations. Improvements in dynamic equations are used to reduce the propagation of navigation errors, and high-rate virtual constraints are used to reduce the impact of bias errors. An information filter is devised to fuse the multi-rate observations from multiple sources, i.e. SINS, CNS and model constraints. The proposed method is also evaluated by long-range ballistic vehicle navigation simulations. The results indicate that the proposed constrained algorithm can address the degradation problem with remarkable accuracy improvements without adding extra sensors, enhancing the SINS-based navigation performance for ballistic applications.

We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.

Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to generate spatio-temporal matrices and spatio-temporal tensors (i.e., multidimensional arrays). We then imposed a sparse bilinear decomposition on the matrices and a sparse multi-linear decomposition on the tensors. Experimental results on a real-world dataset demonstrated the effectiveness of this methodology, with which we show the existence of connection among regions, time, and vessel attributes.