In the Venus capture period, it is difficult for celestial autonomous navigation to satisfy the requirement of high precision. To improve autonomous navigation performance, a Direction, Distance and Velocity (DDV) measurements deeply integrated navigation method is proposed. The “deeply” integrated navigation reflects the fact that the direction and velocity measurements suppress the Doppler effects in the pulsar signals. In the pulsar observation period, the direction and velocity measurements are utilised to compensate for Doppler effects in the pulsar signals. By these means, the residual effects can be ignored. When the direction, distance or velocity measurements are obtained, they are fused to improve the navigation performance. Simulation results demonstrate that the DDV measurements deeply integrated navigation filter converges very well, and provides highly accurate position estimation without a high quality requirement on navigation sensors.

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.

Precise control is a key factor in enabling Unmanned Underwater Vehicles (UUVs) to complete various underwater activities. The development of UUV control rules is mostly based on UUV dynamic models. However, such dynamic models contain unknown hydrodynamic parameters that need to be identified. This paper presents a new method, Laser Line Scanning for Hydrodynamic Parameter Identification (LSHPI), which integrates laser line scanning, decoupled dynamics, and evolutionary optimisation to identify the hydrodynamic parameters of an Autonomous Underwater Vehicle (AUV). In this research, laser images, seen from an on board camera's perspective and created using Open Graphics Library (OpenGL), were used to validate LSHPI's feasibility. The accuracy of the AUV positions and Euler angles obtained by the laser image-based methods were investigated for each decoupled One-Dimensional (1D) motion and the influence of other motion disturbances on the accuracy of the obtained AUV positions or Euler angles was also evaluated. In addition, the accuracy of the surge-related hydrodynamic parameters obtained by LSHPI was investigated under different motion disturbances. Based on the hydrodynamic parameter identification results under different motion disturbances, LSHPI's feasibility was successfully validated.

Global Navigation Satellite Systems (GNSSs) are easily influenced by the external environment. Signals may be lost or become abnormal thereby causing outliers. The filter gain of the standard Kalman filter of a loosely coupled GNSS/inertial navigation system cannot change with the outliers of the GNSS, causing large deviations in the filtering results. In this paper, a method based on a χ2-test and a dynamically adjusting filter gain method are proposed to detect and separately to suppress GNSS observation outliers in integrated navigation. An indicator of an innovation vector is constructed, and a χ2-test is performed for this indicator. If it fails the test, the corresponding observation value is considered as an outlier. A scale factor is constructed according to this outlier, which is then used to lower the filter gain dynamically to decrease the influence of outliers. The simulation results demonstrate that the observation outlier processing method does not affect the normal values under normal circumstances; it can also discriminate between single and continuous outliers without errors or omissions. The impact time of outliers is greatly reduced, and the system performance is improved by more than 90%. Experimental results indicate that the proposed methods are effective in suppressing GNSS observation outliers in integrated navigation.

The objective of this paper is to accurately determine mobile robots' position and orientation by integrating information received from odometry and an inertial sensor. The position and orientation provided by odometry are subject to different types of errors. To improve the odometry, an inertial measurement unit is exploited to give more reliable attitude information. However, the nonlinear dynamic of these systems and their complexities such as different sources of errors make navigation difficult. Since the dynamic models of navigation systems are nonlinear in practice, in this study, a Cubature Kalman Filter (CKF) has been proposed to estimate and correct the errors of these systems. The information from odometry and a gyroscope are integrated using a CKF. Simulation results are provided to illustrate the superiority and the higher reliability of the proposed approach in comparison with conventional nonlinear filtering algorithms such as an Extended Kalman Filter (EKF).

In this paper, a novel method is proposed to generate the matching sequence of an ICCP algorithm for aircraft geomagnetic-aided navigation based on the M coding principle. The length of the matching sequence and the selection of the matching points directly affects the performance of the Iterated Closest Contour Point (ICCP) algorithm. This study proposes an adaptive geomagnetic matching method, ΔM-ICCP, to solve the problem of selecting suitable matching lengths, and matching points, when a vehicle is moving in a highly dynamic environment. First, the △M coding principle is adopted to select the matching points based on the information of the magnetic field, the resolution of the magnetic map, and the accuracy of the magnetic sensor. Then, the problem of selecting parameters for the △M-ICCP algorithm is turned into an optimisation problem, which can be solved by a Binary Particle Swarm Optimisation (BPSO) algorithm. Finally, the algorithm is verified through simulation experiments. The proposed algorithm can provide a basis to determine the matching length of the △M-ICCP algorithm and adaptively adjust the algorithm's parameters according to different trajectories. The algorithm is applicable even in the areas where the fluctuations of Earth's magnetic field are not significant.

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.

In this paper, a response model of an Unmanned Surface Vehicle (USV) with a pod-like propulsion device is established. To improve the robustness of motion control in heavy sea states, an integrated nonlinear feedback course-keeping controller is proposed. First, to establish a response model of a USV with pod-like propulsion, model parameters are obtained by the method of system identification, then an integrated nonlinear feedback control strategy is proposed. The essence of this method is to make the original error signal pass through a nonlinear function, and then the output of this function is used to replace the original error signal. Simulation results show that under ordinary sea states, nonlinear feedback can save up to 34.5% of energy used compared with standard feedback methods; under heavy sea states, this can rise to 40.8%. A set of field experiments were carried out with a USV with pod-like propulsion. Results show that under heavy sea states, the test USV can maintain the target course well, which proves the correctness of the model and the robustness of the proposed method.

To satisfy the needs of precise pin-point landing missions in deep space exploration, this paper proposes a method based on feature line extraction and matching to estimate the attitude and position of a lander during the descent phase. Linear equations for a lander's motion parameters are given by using at least three feature lines on the planetary surface and their two-dimensional projections. Then, by taking advantage of Singular Value Decomposition (SVD), candidate solutions are obtained. Lastly, the unique lander's attitude and position relative to the landing site are selected from the candidate solutions. Simulation results show that the proposed algorithm is able to estimate a lander's attitude and position robustly and quickly. Without an extended Kalman filter, the average errors of attitude are less than 1° and the average errors of position are less than 10 m at an altitude of 2,000 m. With an extended Kalman filter, attitude errors are within 0·5° and position errors are within 1 m at an altitude of 247·9 m.

In the new generation of Global Navigation Satellite Systems (GNSS), dual-frequency constant envelope multiplexing is widely desired and is becoming an important subject in signal design. Considerable work has been devoted to multiplexing for the Alternative Binary Offset Carrier (AltBOC)-like signal model, for which each sideband consists of two or fewer signal components. In this paper, a phase-aligned dual-frequency constant envelope multiplexing technique is proposed for a general dual-frequency signal model. This multiplexing technique can be used to combine two constant-envelope-modulated signals in two sidebands into a composite signal with a constant envelope, where the constant-envelope-modulated signal in each sideband consists of an arbitrary number of signal components with an arbitrary power ratio and phase relationship among the signal components. A Lookup Table (LUT)-based signal generation method is also proposed, for which the required driving clock rate of the signal generator can be flexibly adjusted to meet the requirements of the satellite payload. Applications for the AltBOC-like signal model and a general dual-frequency signal model in the Beidou B1 band validate the flexibility and high multiplexing efficiency of our method. Specifically, AltBOC is a special case of the proposed method.

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.

This paper proposes a simplified algorithm for reducing the computational load of the conventional underwater integrated navigation system. The system usually comprises a three-dimensional accelerometer, a three-dimensional gyroscope, a three-dimensional Doppler Velocity Log (DVL) and a data fusion algorithm, such as a Kalman Filter (KF). Since the expected variations of roll, pitch and depth are small, these quantities are assumed to be constant, and the proposed system is designed in a two-dimensional form. Due to the low speed of the vehicle, the nonlinear dynamic equation of the velocity can be simplified in a linear form. We also simplify the conventional KF in order to avoid matrix multiplications and matrix inversions. The performance of the designed system is evaluated in a sea trial by an Autonomous Underwater Vehicle (AUV). The results show that the proposed system can significantly reduce the computational load of the conventional integrated navigation system without a significant reduction in position and velocity accuracy.

The current positioning accuracy of the BeiDou Navigation Satellite System (BDS) Single Point Positioning (SPP) with code measurement is in the order of several metres due to systematic errors. To further reduce the systematic errors in SPP, this contribution develops a new strategy to BDS SPP with a sparse reference network, named Augmented SPP (A-SPP). In this method, the Combined Residual Errors (CRE) products of BDS B1I measurement are integrated with three optional base stations that are close to the rover station. Based on the Satellite Elevation Angle Weighted (SEAW) average technique, the code residual errors of each BDS satellite observed by the rover station can be acquired epoch-by-epoch. Finally, the corrected code observations for the rover station can be utilised to achieve an A-SPP solution. The validation of this method is confirmed by both static and kinematic tests. Results clearly show that the accuracies of the A-SPP solution for horizontal and vertical directions are better than 0·5 m and 1·0 m. This study suggests that the proposed A-SPP solution is a good option for single-frequency GNSS users to improve their positioning performance.

The safety of maritime transportation has become increasingly important in recent decades. In this paper, a decision model (a multi-objective and multi-layer fuzzy optimisation model) for navigation risk in different sea areas is established. This is done according to the evaluation index system based on relative data extracted and analysed from Automatic Identification Systems (AIS) information and the multi-objective and multi-layer fuzzy optimisation theory. Then, sorted by an optimal relative membership degree vector and calculated from lower layer to higher layer, the sea areas which have higher navigation risk are selected. Finally, the decision model is shown to be scientific and practical since the results from it are basically consistent with real traffic in Chengshantou waters and the results from the fuzzy comprehensive evaluation model. With the decision model, navigation risk judgments in different sea areas can be offered. It can also provide decision making references to the design of ship routing systems, the layout of search and rescue sites, the configuration of rescue forces and the administration of navigation safety.

Parameter uncertainties which may lead to divergence of traditional Kalman filters during Mars entry are investigated in this paper. To achieve high precision navigation, a Derivative-free Nonlinear version of an Extended Recursive Three-Step Filter (DNERTSF) is introduced, which suits nonlinear systems with arbitrary parameter uncertainties. A DNERTSF can estimate the state and the parameters simultaneously, and Jacobian and Hessian calculations are not necessary for this filter. Considering the uncertainties in atmosphere density, ballistic coefficient and lift-to-drag ratio, a numerical simulation of Mars entry navigation is carried out. Compared with the standard Unscented Kalman Filter (UKF), DNERTSF can effectively reduce the adverse effects of parameter uncertainties and achieve a high navigation accuracy performance, keeping position and velocity estimation errors at a very low level. In all, the DNERTSF in this paper shows good advantages for Mars entry navigation, providing a possible application for a future Mars pinpoint landing.

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.

The Safety Level Approach (SLA) is a structured application of risk-based methodologies for the International Maritime Organization's (IMO's) rule-making process. When the SLA is applied, safety goals have to be provided. In order to set appropriate levels for safety goals, it is necessary to measure the tolerance degree of the current safety level. Based on the consistency with individual risk criteria and the principle of continuous improvement, this paper proposes an approach to establish the societal risk criteria, which can be used for setting safety goals in the context of the IMO's SLA. Furthermore, by defining dynamic factors to express risk aversion, a method for tolerance measurement of the current societal risk is developed. Finally, a case study into the societal risk evaluation of cruise ships and Roll-On Roll-Off (RO-RO) passenger ships is conducted.

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.

To track multiple ships and estimate the feature parameters of multiple emitters on board using electronic intelligence satellites under clutter interference, a long and random revisit time, and other complex conditions, a novel tracking algorithm using both kinematic (position and velocity) and feature information based on an improved Multiple Hypothesis Tracking (MHT) approach is proposed in this paper. Firstly, the characteristics of multi-ship tracking with multiple emitters using satellite electronic information are analysed, and a new model of an emitter is built as an extended target in geographical coordinates. Secondly, a pre-processing of measurements is utilised via hierarchical clustering using the location and feature information of emitters. Thirdly, feature information is incorporated into the MHT framework using Jensen-Shannon divergence distance and fuzzy C-means clustering to calculate track scores. Finally, we present the prediction and update of target states, especially the update of feature parameters, to realise joint kinematic and feature tracking of ships. The results of the simulation show that the proposed method has much better tracking performance than the standard MHT algorithm.

High-precision underwater positioning must eliminate the influence of refraction artefacts. Since a Time Of Arrival - Global Navigation Satellite System Intelligent Buoys (TOA-GIB) system does not measure incident beam angles, common refraction correction methods cannot be directly used for refraction artefacts. An Equivalent Sound Speed (ESS) iteration method is proposed and is based on the transformation relations between depth, the ESS gradient and the incident beam angle. On this basis, a TOA/AOA-GIB system without a real-time Sound Speed Profile (SSP) is proposed to estimate the target position and the ESS gradient as unknown parameters. The results from a simulation experiment show that the positioning accuracy of a TOA/AOA-GIB system is better than 0·07% of water depth when the accuracy of the incident beam angle is 0·1°.