For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.

An electronic navigational chart is a major source of information for the navigator. The component that contributes most significantly to the safety of navigation on water is the information on the depth of an area. For the purposes of this article, the authors use data obtained by the interferometric sonar GeoSwath Plus. The data were collected in the area of the Port of Szczecin. The samples constitute large sets of data. Data reduction is a procedure to reduce the size of a data set to make it easier and more effective to analyse. The main objective of the authors is the compilation of a new reduction algorithm for bathymetric data. The clustering of data is the first part of the search algorithm. The next step consists of generalisation of bathymetric data. This article presents a comparison and analysis of results of clustering bathymetric data using the following selected methods: K-means clustering algorithm, traditional hierarchical clustering algorithms and self-organising map (using artificial neural networks).

In this paper the Grid correction method (GRID) was proposed for the first time as an interpolation method of the double differenced ionospheric delay for GNSS Network RTK. The Distance Interpolation Method (DIM), Linear Interpolation Method (LIM), the Kriging Interpolation Method (KRG) and the GRID are described and compared in the aspect of their interpolation performance. It was shown in the tests that the GRID interpolation method performed better than the other three. For mid-latitude stations, its interpolation accuracy is better than 1 cm, and the extrapolation accuracy is better than 2 cm. Furthermore, the GRID method can achieve a good performance even at low satellite elevation, and also its interpolation accuracy can be better than 2 cm at low latitudes.

This paper focusses on the development of a tool for decision-making, tailored for personnel involved in complex Ship-To-Ship (STS) operations, to enhance the efficiency and safety of these operations. A step-wise approach has been selected. The first step includes specification, development and testing of the tool in a simulated work environment using full-mission simulators. In the second step the findings from application of the tool in the simulated work environment will be used to develop a prototype which will be tested during real life STS operations. This paper describes work done in the first of these two steps. During four iterations, a Graphical User Interface (GUI) has been made following Interaction Design (IxD) principles. The designs have been iteratively developed and tested by experienced ship's officers in a ship-handling simulator to clarify key information to enhance their Situation Awareness (SA) and decision-making process. In order to find indicators for performance, an initial performance test was carried out in a ship-handling simulator. The test indicates that a logic based Decision-Support System (DSS) can improve existing simulator-based training activities in STS operations.

The information on the Closest Point of Approach (CPA) of another vessel to own ship is required in a potential collision situation as it helps determines the risk to each vessel. CPA is usually calculated based on the speed and direction of the approaching ship neglecting the Change Of Speed (COS) and the Rate Of Turn (ROT). This will make the CPA less useful. To improve the CPA calculation, Automatic Identification System (AIS) information containing the Speed Over Ground (SOG), Course Over Ground (COG), COS and ROT is used. Firstly, a model using these four factors is built to predict ship positions better. Secondly, a three-step CPA searching method is developed. The developed CPA calculation method can assist in informing the navigation decisions and reducing unnecessary manoeuvres. Through the analysis of a real collision scenario, this paper shows that the proposed method can help identify and warn of anomalous ship behaviours in a realistic time frame.

A strapdown inertial navigation system and celestial navigation system integrated autonomous navigation scheme is proposed in this paper, using the navigation information obtained from Earth sensors and star sensors. To eliminate the adverse effect caused by the asymmetry of Earth infrared radiance, the relationship between Earth infrared radiance brightness and effective horizon height is found. According to the relationship as well as the measuring principle of the Earth sensor, this paper derives a function to correct the measurement of the Earth sensor. Then, the angle-distance of stars can be calculated, and using this information, we can estimate the navigation information of a ballistic missile by least square estimation. The simulation results show that the error of Earth infrared radiance has a great effect on the navigation precision, and by using the correction scheme, this adverse effect can be greatly mitigated. This correction scheme is available and effective.