This paper proposes a novel sensor fusion approach using Ultra Wide Band (UWB) wireless radio and an Inertial Navigation System (INS), which aims to reduce the accumulated error of low-cost Micro-Electromechanical Systems (MEMS) Inertial Navigation Systems used for real-time navigation and tracking of mobile robots in a closed environment. A tightly-coupled model of INS/UWB is established within the integrated positioning system. A two-dimensional kinematic model of the mobile robot based on kinematics analysis is then established, and an Auto-Regressive (AR) algorithm is used to establish third-order error equations of the gyroscope and the accelerometer. An Improved Adaptive Kalman Filter (IAKF) algorithm is proposed. The orthogonality judgment method of innovation is used to identify the “outliers”, and a covariance matching technique is introduced to judge the filter state. The simulation results show that the IAKF algorithm has a higher positioning accuracy than the KF algorithm and the UWB system. Finally, static and dynamic experiments are performed using an indoor experimental platform. The results show that the INS/UWB integrated navigation system can achieve a positioning accuracy of within 0·24 m, which meets the requirements for practical conditions and is superior to other independent subsystems.

Maritime traffic has significantly increased in recent decades due to its advantageous costs, delivery rate and environmental compatibility. With the advent of the new generation of marine radars, based on the solid-state transmitter technology that calls for much longer transmitted pulses, the interference problem can become critical. Knowing the positions and the heights of the ships, the mean number of the vessels in radar range can be estimated to evaluate the effects of their mutual radar interferences. This paper aims to estimate the probability density function of the mutual distances. The truncation of the density function within a limited area related to horizon visibility leads to a simple single-parameter expression, useful to classify the ships as either randomly distributed or following a defined route. Practical results have been obtained using Automatic Identification System (AIS) data provided by the Italian Coast Guard in the Mediterranean Sea.

The container liner shipping industry has stepped into an era of international strategic alliances. Important to these liner alliances is the sharing and allocation of container slots between its member carriers. This paper optimises planning of container ship capacity sharing and co-allocation under a co-charting agreement. First, we explain the concept of this business agreement and its implications on maritime operations. Then, we identify key influencing factors that may affect the decisions of cooperative slot co-allocation. The slot co-allocation problem is modelled as an Integer Programming problem and solved using data from two routes between the United States and Asia. The model determines the optimal slot co-allocation strategies between shipping alliance carriers along allied shipping routes. Computational results indicate that the proposed method is effective in obtaining optimal, cooperative slot sharing strategies that can maximise the total system revenue.

The aim of the Integrated Navigation System (INS) on a ship bridge should be to provide the navigator with added value and aid in the complex task of conducting a safe and efficient passage at high speeds in demanding waters. This article presents a method for analysing eye tracking data to reveal sub-optimal design in the bridge layout and in the software graphical user interface on a maritime navigation display. The analysis of eye tracking data with a focus on scan path events indicates sub-optimal design, and the paper provides suggestions for improvement in design and interfaces. Pros and cons of using Eye Tracking Glasses in a maritime environment are presented. The importance of not affecting the normal behaviour of the navigator by collecting data is stressed, and how the software should provide good visualisation and interpretation of the eye tracking data.

The possibility of identifying the type of multipath environment and receiver motion (e.g. pedestrian, vehicular) using pattern recognition approaches based on multipath parameters is investigated. This allows the receiver to adjust its tracking strategy and optimally tune its tracking parameters to mitigate code multipath effects. A Support Vector Machine (SVM) classification method with a modified Gaussian kernel is applied in this approach. A set of temporal and spectral features is extracted from the correlation samples of the received signals in different environments to train the classifier. The latter is then used in the structure of stochastic gradient-based adaptive multipath compensation and tracking techniques to tune the signal tracking parameters based on the environment and receiver motion. Simulation and real data measurements using Galileo E1B/C signals are performed to assess the validity of the proposed environment identification approaches and to evaluate the impact of the proposed context-based receiver parameter tuning techniques on tracking performance in multipath environments. Test results showed that the proposed classifiers have an accuracy between 86% and 92%, and the tracking performance improved by about 15%.

A method is proposed for improving the accuracy and self-consistency of bathymetric maps built using an Autonomous Underwater Vehicle (AUV) to create precise prior maps for Terrain-Aided Navigation (TAN), when the Global Positioning System (GPS) or another precise location method is unavailable. This method consists of front-end and back-end. For the front-end, the AUV predicts the measurement of the bathymetry system through Terrain Elevation Measurement Extrapolation Estimation (TEMEE) and calculates the likelihood function using real measurements. After the final Inertial Navigation System (INS) error is obtained by communicating with sensor nodes, the process enters the back-end. A Terrain Correlation Correction Method (TCCM) and an Improved Terrain Correlation Correction Method (ITCCM) are proposed to solve the gradual distribution of the final INS error to each point on a path, and the accuracy of ITCCM was confirmed experimentally. Finally, a TAN simulation experiment was conducted to prove the importance and necessity of map correction using ITCCM. ITCCM was proven to be an effective and important method for correcting maps built using an AUV.

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.

Serious accidents in the marine and offshore industry have underscored the need for safety evaluation of maritime operations using risk and safety analysis methods which have become a powerful tool in identifying technical solutions and operational management procedures. Given that Fault Tree Analysis (FTA) is a known methodology used for analysing engineering systems, the approach is usually conducted using known failure data. But most offshore operations are conducted in a challenging and uncertain environment and the failure data of some of these systems are usually unavailable requiring a flexible and yet robust algorithm for their analysis. This paper therefore seeks to analyse the complex structure of Offshore Supply Vessel (OSV) collision with platforms by incorporating a Fuzzy Fault Tree Analysis (FFTA) method. Fuzzy set theory provides the flexibility to represent vague information from the analysis process. The methodology is structured in such a manner that diverse sets of data are integrated and synthesized for analysing the system. It is envisaged that the proposed method could provide the analyst with a framework to evaluate the risks of collision enabling informed decisions regarding the deployments of resources for system improvement.

The Great Elliptic Sailing (GES), which can reduce sailing distance, is important to navigators. Whether a Great Ellipse (GE) is worth using depends on whether the distance saved is significant. Otherwise, the Rhumb Line (RL) is easier to steer. We propose a simple criterion to evaluate the difference in distance between the GE and the RL. The criterion is that the GE is worth using when the vertex lies between the departure and destination. In order to take the advantage of shorter distance, the GE is usually approximated as a series of waypoints. Unlike currently practised methods, we propose the Longitude Bisection Method (LBM) which determines waypoints with varying intervals. This approach can establish the appropriate number of waypoints to approximate the GE effectively. The proposed criterion and the LBM are demonstrated in practical examples.

Ship collision avoidance involves helping ships find routes that will best enable them to avoid a collision. When more than two ships encounter each other, the procedure becomes more complex since a slight change in course by one ship might affect the future decisions of the other ships. Two distributed algorithms have been developed in response to this problem: Distributed Local Search Algorithm (DLSA) and Distributed Tabu Search Algorithm (DTSA). Their common drawback is that it takes a relatively large number of messages for the ships to coordinate their actions. This could be fatal, especially in cases of emergency, where quick decisions should be made. In this paper, we introduce Distributed Stochastic Search Algorithm (DSSA), which allows each ship to change her intention in a stochastic manner immediately after receiving all of the intentions from the target ships. We also suggest a new cost function that considers both safety and efficiency in these distributed algorithms. We empirically show that DSSA requires many fewer messages for the benchmarks with four and 12 ships, and works properly for real data from the Automatic Identification System (AIS) in the Strait of Dover.

The purpose of this study is to determine the navigation efficiency for transiting from various ports in Asia and Rotterdam in Europe via the Northern Sea Route. Navigation efficiency was derived from fuel consumption ratios. Preliminary research results showed that fuel consumption and the amount of carbon dioxide emissions was reduced by transiting from various ports through the European Sea Route, and the navigation efficiencies of the various ports ranked in the following descending order: Yokohama - Busan - Shanghai - Kaohsiung - Hong Kong - Singapore. The higher the ratio was, the lower the navigation efficiency of using the Northern Sea Route, i.e. more fuel was consumed and travel costs were higher. In contrast, the lower the ratio was, the higher the navigation efficiency, i.e. less fuel was consumed and travel costs were lower. Recommendations were discussed in the study.

In the effort to quantify Automatic Dependent Surveillance Broadcast (ADS-B) system safety, the authors have identified potential ADS-B failure modes in Syd Ali et al. (2014). Based on the findings, six potential hazards of ADS-B are identified in this paper. The authors then applied the Probabilistic Safety Assessment approach which includes Fault Tree Analysis (FTA) and Importance Analysis methods to quantify the system safety. FTA is applied to measure ADS-B system availability for each identified hazard while Importance Analysis is conducted to identify the most significant failure modes that may lead to the occurrence of the hazards. In addition, risk significance and safety significance of each failure mode are also identified. The result shows that the availability for the ADS-B system as a sole surveillance means is low at 0·898 in comparison to the availability of ADS-B system as supplemental or as primary means of surveillance at 0·95 and 0·999 respectively. The latter availability values are obtained from Minimum Aviation System Performance Standards (MASPS) for Automatic Dependent Surveillance-Broadcast (DO-242A).

An X-ray pulsar/starlight Doppler deeply-integrated navigation method is proposed in this paper. A starlight Doppler measurement-aided phase propagation model, which can remove the orbital effect within the recorded photon Time Of Arrivals (TOAs), is derived, and guarantees that the pulse phase can be extracted from the converted photon TOAs using computationally efficient methods. Some simulations are performed by a hardware-in-loop system to verify the performance of the integrated pulse phase estimation method as well as of the integrated navigation method. The integrated pulse phase estimation method could achieve an estimation performance similar to the existing method for orbiting vehicles at the cost of much less computational complexity, is capable of handling the signals of millisecond pulsars, and is applicable to various vehicles. In addition, the proposed integrated navigation method could provide reliable positioning results for various vehicles.

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.

This paper offers an algorithm for enhancement of positioning accuracy of a quad-rotor flying robot, based on jerk and jounce of motion. The suggested method utilises the first and second numerical derivatives of the vehicle's acceleration and augments the mathematical model in the estimation process. For this purpose, the Kalman Filter (KF) is implemented for integration of a Strapdown Inertial Navigation System (SINS) and Global Navigation Satellite System (GNSS). The required data are collected from a low-cost/quality Micro Electromechanical Sensors (MEMS) during an assisted flight. For increasing the precision and accuracy of the collected data, all instruments including accelerometers, gyroscopes and magnetometers are calibrated before the experiments. Moreover, to reduce and limit the measurement noises of the MEMS sensor, a low-pass filter is applied; this is while sensors in the autopilot are affected by high levels of noise and drift, which makes them inappropriate for accurate positioning. The experimental results exhibit an improvement in positioning and altitude sensing through augmentation of the loosely coupled SINS/GNSS navigation method.

This paper is concerned with the preliminary localisation, orbit determination and model-based path forecasting of space debris based on a robust procedure. In this work, an in-orbit observer utilises only relative bearing observations iteratively. To this end, the problem is first formulated in order to calculate the distance vector between the space debris and any orbiting observer. Afterwards, the obtained position vector is corrected through an Extended Kalman Filter (EKF) for shrinking the sensor and process errors and increasing robustness of the computations in the presence of uncertainties. After preliminary positioning, the related classical orbital elements are acquired via the predicted position and velocity vectors using a hybrid technique. Extensive simulations demonstrate the efficacy and robustness of the aforementioned method, and in particular it is verified that the proposed scheme is capable of producing a suitable solution for preliminary localisation and orbit determination of space debris based on the presented space-based observation, which is practical in phasing and chasing manoeuvres of any grabber space robot.

Nonlinear least squares estimations have been widely applied in positioning. However, nonlinear least squares estimations are generally biased. As the Gauss-Newton method has been widely applied to obtain a nonlinear least squares solution, we propose an iterative procedure for obtaining unbiased estimations with this method. The characteristics of the linearization error are discussed and a systematic error source of the linearization error needs to be removed to guarantee the unbiasedness. Both the geometrical condition and the statistical condition for unbiased nonlinear least squares estimations are revealed. It is shown that for long-distance observations of high precision, or for a positioning configuration with the lowest Geometric Dilution Of Precision (GDOP), the nonlinear least squares estimations tend to be unbiased; but for short-distance cases, the bias in the nonlinear least squares solution should be estimated to obtain unbiased values by removing the bias from the nonlinear least squares solution. The proposed results are verified by the Monte Carlo method and this shows that the bias in nonlinear least squares solution of short-distance distances cannot be ignored.

The Automatic Identification System (AIS) receiver on board the main satellite of the TianTuo-3 constellation, LvLiang-1, is a new generation of AIS receiver. Having partly solved the signal conflict problems and with larger coverage over the ground, the AIS receiver on board TianTuo-3 greatly improves the signal detection ability. The data received by the AIS receiver during the TianTuo-3 debugging stage is employed for detailed analysis in this paper. Results include: TianTuo-3 implements four-frequency detection at the same time, and a time-flag is inserted into the received AIS data, a small portion of Class A vessels (at least 1480) have been equipped with AIS sending the long range AIS broadcast message with two new frequency channels and the hourly averaged count of the message received by TianTuo-3’s AIS is between 1500 ~ 2500. This AIS receiver is capable of real-time tracking a single vessel. In conclusion, the TianTuo-3 space-based AIS receiver is capable of continuously receiving AIS messages sent by global maritime vessels.