Oscillator phase noise has a negative effect on the tracking performance of Global Navigation Satellite System (GNSS) receivers. To provide GNSS software receivers with real test environments, this paper proposes a method to simulate the GNSS Intermediate Frequency (IF) signal, taking the oscillator phase noise effect into consideration. The oscillator parameters are first measured via a pseudolite transmitter and receiver system. According to the measured oscillator parameters, an oscillator-induced frequency fluctuation is then generated, and added to the digital IF signal. Further simulation experiments are conducted that attempt to measure the oscillator phase noise effect on a second-order Phase Lock Loop (PLL) performance. Results indicate that the IF signal simulator considering the oscillator phase noise is able to provide software receivers with real signal dynamics, helping to evaluate the performance of signal processing algorithms on a software platform.

Real-time Precise Point Positioning (PPP) relies on the use of accurate satellite orbit and clock corrections. If these corrections contain large errors or faults, either from the system or by meaconing, they will adversely affect positioning. Therefore, such faults have to be detected and excluded. In traditional PPP, measurements that have faulty corrections are typically excluded as they are merged together. In this contribution, a new PPP model that encompasses the orbit and clock corrections as quasi-observations is presented such that they undergo the fault detection and exclusion process separate from the observations. This enables the use of measurements that have faulty corrections along with predicted values of these corrections in place of the excluded ones. Moreover, the proposed approach allows for inclusion of the complete stochastic information of the corrections. To facilitate modelling of the orbit and clock corrections as quasi-observations, International Global Navigation Satellite System Service (IGS) real-time corrections were characterised over a six-month period. The proposed method is validated and its benefits are demonstrated at two sites using three days of data.

The acquisition of signals is a precondition for tracking and solution calculation in software-based Global Navigation Satellite System (GNSS) receivers. The Parallel Code phase Acquisition (PCA) algorithm can simultaneously obtain the correlation results at every sampling point. However, if the number of sampling points that needs processing is large, this method will lead to a heavy computational load. Thus, we improve the process of the PCA algorithm and propose a novel algorithm that divides the signals into K (K is a constant) parts to achieve correlation and obtains the correlation results with a fusion algorithm. This algorithm can simultaneously obtain the correlation results for sampling points at an interval of K points. If the K value is selected appropriately, the computational load can be decreased by about 50%. Also, the Receiver Operating Characteristic (ROC) curves show that under a certain probability of false alarm, the detection probability of the proposed algorithms is 5% lower than that of the PCA algorithm. Therefore, the proposed algorithm can speed up the acquisition process with a slight decrease in detection probability.

This study develops an ordered probit model to evaluate the factors influencing two-ship collision severity using ten years’ ship collision accident data from Fujian sea areas. The model results show that the involvement of big ships has the largest impact in increasing the probability of a serious or very serious accident, followed by the involvement of fishing vessels. There will be a bigger probability of a serious accident if both ships involved in the collision are cargo ships. We found that the season of spring, poor visibility and night time periods are more likely to be factors in high severity levels of ship collision. The results also reveal that lookout failure plays a decisive role in increasing serious accident risk compared with other types of human errors. The results of this study may be beneficial for policy-makers in proposing efficient strategies to reduce the likelihood of serious ship collisions.

In this paper, a novel Strong Tracking Sigma-Point Predictive Variable Structure Filter (ST-SP-PVSF) is presented as a further development of the Adaptive Predictive Variable Structure Filter (APVSF) for attitude synchronisation during Satellite Formation Flying (SFF). First, the sequence orthogonal principle is adopted to enhance the robustness of the APVSF for any nonlinear system with uncertain model errors. Then, sigma-point sampling strategies (such as unscented transfer, cubature rule and Stirling's polynomial interpolation) are introduced to extend the APVSF with the ability to capture the second central moment's information on the model errors to update the system model with higher precision. The new methodology has advantages in dealing with the various types of uncertainties or model errors compared with the APVSF. In addition, it does not need to choose the limit boundary layer ψlim it for system estimation, which reduces the sensitivity to the initial parameters and improves its adaptive ability over the APVSF. Simulations are performed to demonstrate that the proposed method is more suitable for attitude synchronisation estimation of the SFF system.

This study aims to address the accuracy evaluation problem for rapid transfer alignment with the coexistence of large misalignment angles and uncertain observation noises. For the requirement of accuracy evaluation, complete information in terms of misalignment angles should be estimated during the alignment process. Thus, a fixed-interval smoothing approach is the core of solving this problem. In this paper, a new Divided Difference Filter (DDF)-based an Interacting Multiple Model Two-Filter Smoother (IMM-TFS) is developed to estimate the misalignment angles. The proposed DDF-based IMM-TFS releases the restriction of inverse nonlinearity by using the weighted statistical linearization regression method, and the resulting pseudo-linear model can be used for backward-time IMM filtering. The smoothing step takes into account the merging of estimations and the interaction of multiple models simultaneously. The new smoother is compared with the previous well-known methodologies in simulations. The results show that the DDF-based IMM-TFS can achieve better accuracy for misalignment angles estimation, and has a high efficiency for detecting the changes in a model.

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.

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.

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 BeiDou navigation satellite system (BDS) has been providing a regional service in the Asia–Pacific area since 27 December 2012, and a new Inclined Geosynchronous Satellite Orbit (IGSO) satellite IGSO-6 joined the 14-satellite constellation in operation on 29 March 2016. In this paper, the signal and positioning performance of IGSO-6 are assessed. Compared with other IGSOs, the carrier-to-noise-density ratios of IGSO-6 show comparable performance for the B3 signal and a lower power level for the B2 signal, while the B1 signal is more powerful and has the lowest noise and multipath errors. The satellite-induced code bias of IGSO-6 was investigated and indicates that IGSO-6 has similar characteristics to other IGSOs. The different inter-frequency bias variations among IGSOs with daily periodicity are demonstrated. The BDS positioning performances with IGSO-6 were investigated in Single Point Positioning (SPP) and Precise Point Positioning (PPP) modes at the 95% confidence level. For SPP, there was an improvement of about 4·9% and 3·6% in the horizontal and vertical components, respectively. The convergence time was improved by about 18·3% and 17·8% in the horizontal and vertical components for positioning accuracy to be better than 50 cm, respectively.

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.

Vulnerability of satellite-based navigation signals to intentional and unintentional interference calls for a high-level overview of Global Navigation Satellite System (GNSS) threats occurring globally to understand the magnitude and evolution of the problem. Therefore, a mechanism needs to be developed whereby disparate monitoring systems will be capable of contributing to a common entity of basic information about the threat scenarios they experience. This paper begins with a literature survey of 37 state-of-the-art GNSS threat monitoring systems, which have been analysed based on their respective operational features - constellations monitored and whether they possess the capability to perform interference-type classification, spoofing detection, and interference localisation. Also described is a comparative analysis of four GNSS threat reporting formats in use today. Based on these studies, the paper describes the Horizon2020 Standardisation of GNSS Threat Reporting and Receiver Testing through International Knowledge Exchange, Experimentation and Exploitation (STRIKE3) proposed integrated threat monitoring demonstration system and related standardised threat reporting message, to enable a high-level overview of the prevailing international GNSS threat scenarios and its evolution over time.

Fine initial alignment is vital to the Inertial Navigation System (INS) before the launching of a missile. The existing initial alignment methods are mainly performed on a stationary base after the missile has been erected to the vertical state. However, these methods consume extra alignment time and some state variables have poor degrees of observability, thus losing the rapidity of alignment. In order to solve the problem, a fast fine initial self-alignment method of a missile-borne INS is proposed, which is performed during the erecting process on a stationary base. The convected Euler angle error is modelled to optimise the erecting manoeuvre which can prevent large Euler angle errors and improve the system observability. The fine initial alignment model is established to estimate and correct the initial misalignment. Several experiments verify that the proposed method is effective for improving the rapidity of the fine initial alignment for a missile-borne INS.

Based on our previously developed ship collision avoidance steering system, this paper develops a more extensive collision avoidance decision-making system for non-uniformly moving ships. A real-time simulator based on the Six-Dimensional (6D) Manoeuvring Modelling Group (MMG) model is used to simulate the ship's motion. To validate the manoeuvring mathematical model, sea trial measurements of a container ship (C-3) have been selected. This study incorporates Nomoto's second-order model into a numerical model to calculate the turning characteristics of the ship. The manoeuvring indices of Nomoto's model are the knowledge base of the simplified ship simulation model. To verify the ship collision avoidance system with respect to different traffic factors, simple and complex collision avoidance cases have been designed in fast-time simulations with multi-ship encounter conditions. The simplified simulation model developed here can quickly determine the helm angle when the ship makes a collision avoidance manoeuvre, which is helpful for the safety of ship navigation in heavy traffic areas.

A Position and Orientation System (POS) integrating an Inertial Navigation Systems (INS) and the Global Positioning System (GPS) is a key component of remote sensing motion compensation. It can provide reliable and high-frequency high-precision motion information using a Kalman Filter (KF) during GPS availability. However, the performance of a POS significantly degrades during GPS outages. To maintain reliable POS outputs, this paper proposes a new hybrid predictor based on modelling the nonlinear time-series data-driven INS-errors using Noisy Input Gaussian Process Regression (NIGPR), which takes the input noise into account. The proposed approach is used to learn the nonlinear INS-errors model when GPS signals are available. When GPS outages occur, it starts to predict the observation measurement, and then feeds it to a KF as a virtual update to estimate all the INS errors. The proposed approach is verified in a real airplane, which combines a POS and Synthetic Aperture Radar (SAR). Experimental results show that the proposed approach significantly improves the performance of the POS, with improvements more than 90% better than a KF and 10% better than a Gaussian Process Regression (GPR/KF) combination during various GPS outages.

Motion compensation is a significant part of an airborne remote sensing system. A Position and Orientation System (POS) can directly measure the motion information of an airborne remote sensing payload that can improve the quality of airborne remote sensing images. Gravity disturbance, information on which is often ignored due to being difficult to acquire in real-time, has become the main error source of POS in the development of inertial components. In this paper, a new real-time gravity compensation method is proposed, which includes the gravity disturbance as the error states of a POS Kalman filter, and an accurate gravity disturbance model is constructed using a time-varying Gaussian-Markov model based on a high-precision gravity map, whose resolution is enhanced by a new interpolation method based on Gaussian Process Regression (GPR). A flight experiment was conducted to evaluate the efficiency of the proposed method and the results showed that the proposed method performs well when compared with other real-time gravity compensation methods.

A novel robust scheme for Transfer Alignment (TA) is proposed for improving the accuracy of the navigation of a Hypersonic Cruise Vehicle (HCV). The main goal is to instil robustness in the safety and accuracy of the attitude determination, despite mode uncertainties. This article focuses on Robust Sparse-Grid Quadrature Filtering (R-SGQF) using two given robust factors for norm-bounded model uncertainties in non-linear systems. Missile dynamic and measurement model uncertainties are established to validate TA technologies. The nominal stability of the R-SGQF is defined by estimating error dynamics. The technique gives sufficient conditions for the R-SGQF in terms of two parameterised Riccati equations. Robust stability is analysed using Lyapunov theory and the accuracy level of the Sparse-Grid Quadrature (SGQ) algorithm. Embedding the SGQ technique into the robust filter structure, R-SGQF is not only of robust stability against uncertainty but also of higher accuracy. The simulation results of the TA algorithm demonstrate that attitude determinations validate the effectiveness of the R-SGQF algorithm.

Synchronisation of the received Pseudorandom (PRN) code and its locally generated replica is fundamental when estimating user position in Global Navigation Satellite System (GNSS) receivers. It has been observed through experiments that user position accuracy decreases if sampling frequency is an integer multiple of the nominal code rate. This paper provides an accuracy analysis based on the number of samples and the residual code phase of each code chip. The outcomes reveal that the distribution of residual code phases in the code phase range [0, 1/ns), where ns is the number of samples per code chip, is the root cause of accuracy degradation, rather than the ratio between sampling frequency and nominal code rate. Doppler frequencies, coherent integration periods, front-end filter bandwidths and received Carrier to Noise ratios (C/N0) also influence receiver accuracy. Also provided are a sampling frequency selection guideline and new proposed estimates of the correlation output and the Delay Locked Loop (DLL) tracking error, which can be applied to precisely model GNSS receiver baseband signal processing.