A robust adaptive nonlinear asymptotic regulating control law is designed for dynamically positioned vessels exposed to unknown time-varying external disturbances incorporating Fuzzy Logic Systems (FLSs), projection operators, and the “robustifying” term into the vectorial backstepping technique. The FLSs approximate the vessel unknown dynamics and the update laws based on the online projection operators update the fuzzy weight vectors. The robustifying term handles the external disturbances and the fuzzy approximation errors. The designed Dynamic Positioning (DP) control law achieves asymptotic regulation of the vessel's position and heading and makes the other signals in the DP closed-loop control system of vessels be uniformly ultimately bounded. Simulations based on the Marine System Simulator toolbox validate the designed DP control law.

In this study, the effect of acceleration matching according to sensor specifications in rapid transfer alignment is analysed. In general, the velocity and attitude information of the Master Inertial Navigation System (MINS) is used for transfer alignment. MINS angular velocity information is used to improve the alignment speed in shipboard transfer alignment. Acceleration matching, on the other hand, is generally considered an impractical option for transfer alignment. However, in the case of shipboard transfer alignment, acceleration matching is thought to be effective. In order to analyse the performance of acceleration matching, a performance index is defined and the efficiency of acceleration matching is analysed according to various sensor specifications and simulation environments. Based on the analysis of the estimated performance according to the simulation results, it is confirmed that acceleration matching in rapid transfer alignment is valid.

This paper presents an investigation of the precise point positioning (PPP) performance of a combined solution from BDS-2 and BDS-3 satellites. To simultaneously process different BDS signal observations, i.e., B1/B1C, B2/B2a and B3C, undifferenced and uncombined observations with ionosphere delay constrained by the deterministic plus stochastic ionosphere model are used in the basic model. Special attention is paid to code bias and receiver clock parameters in the derivation of the observation model. The analysis is carried out using more than one-month data for BDS-2 and BDS-3 collected at the CANB, DWIN, KNDY and PETH stations in the Asia-Pacific region. The results suggest that compared with BDS-2 alone, the BDS-2 and BDS-3 solution provides significantly more accurate PPP, with increases of 28%, 21% and 5% in the up, north and east directions, respectively. In addition, the average root mean square error decreases to 0·21, 0·13 and 0·16 m for the three directions. Furthermore, the PPP convergence time for BDS-2 and BDS-3 is about 1·5 h and less than 1 h for the horizontal and vertical components, respectively, whereas that for BDS-2 alone is about 2·3 h for both directions.

Fluctuation of the received signal strength (RSS) is the key performance-limiting factor for Wi-Fi indoor positioning schemes. In this study, the Manhattan distance was used in the weighted K-nearest neighbour (WKNN) algorithm to improve positioning accuracy. Reference point (RP) intervals were optimised to reduce the complexity of the system. Specifically, two new positioning schemes are proposed in this paper. Scheme 1 uses the cellular network to refine the fingerprint database, while Scheme 2 uses the cellular network positioning to locate the node a priori, then uses the Wi-Fi network to further improve accuracy. The experimental results showed that the average positioning error of Scheme 1 was 1·60 m, a reduction of 12% compared with the existing Wi-Fi fingerprinting schemes. In Scheme 2, when double cellular networks were used, RP usage was reduced by 64% and the calculating time was 0·24 s, a reduction of up to 69·5% compared with the Manhattan-WKNN algorithm. These proposed schemes are suitable for high accuracy and real-time positioning situations, respectively.

Avoidance of collisions at sea is crucial to navigational safety. In this paper, we use a distributed algorithm to communicate the entire collision avoidance trajectory information for each ship. In each communication, we suggest a new improvement function considering safety and efficiency to identify the avoidance ship in each cycle. Considering the nonlinear collision avoidance trajectory of ships, a new method for calculating the degree of danger using a velocity obstacle algorithm is proposed. Therefore, in each communication, each ship considers the avoidance behaviours of other ships in planning its avoidance trajectory. Additionally, we combine bi-criterion evolution (BCE) and the ant lion optimiser to plan the entire collision avoidance path. Three scenarios are designed to demonstrate the performance of this method. The results show that the proposed method can find a suitable collision-free solution for all ships.

Real-time and accurate fault detection and isolation is very important to ensure the reliability and precision of integrated inertial navigation and global navigation satellite systems. In this paper, the detection performance of a residual chi-square method is analysed, and on this basis an improved method of fault detection is proposed. The local test based on a standardised residual is introduced to detect and identify faulty measurements directly. Differing from the traditional method, two appropriate thresholds are selected to calculate the weight factor of each measurement, and the gain matrix is adjusted adaptively to reduce the influence of the undetected faulty measurement. The sliding window test, which uses past measurements, is also added to further improve the fault detection performance for small faults when the local test based on current measurements cannot judge whether a fault has occurred or not. Several simulations are conducted to evaluate the proposed method. The results show that the improved method has better fault detection performance than the traditional detection method, especially for small faults, and can improve the reliability and precision of the navigation system effectively.

The Strait of Istanbul is one of the world's busiest, narrowest and most winding waterways. As such, there is a high grounding probability for vessels. Although a number of grounding probability models exist, they have been deemed unsuitable by local maritime experts, due to their insufficient stopping distance criteria for narrow waterways. Thus, there is a need for a new model. This paper proposes a two-component grounding probability model that multiplies the geometric grounding probability (calculated with a kinematic-based model) with the causation probability (calculated with a specially designed Bayesian network). The geometric probability model is improved in terms of stopping distance parameters and the Bayesian network is crafted for narrow waterways. The model is then deployed with pre-determined parameters within the Strait of Istanbul to run risk analysis scenarios. The results, validated with actual grounding records, show that the causation probability is the key component for quantifying the probability of grounding in narrow waterways. If navigated without frequent evasive manoeuvres, grounding would be almost inevitable. Although this study focuses on the Strait of Istanbul, the proposed approach can be applied to research into grounding probability of vessels navigating through other waterways.

Carrier-phase inter-frequency bias (IFB) exists in GLObal NAvigation Satellite System (GLONASS) baselines when receivers of different brands are used. Those biases need to be calibrated in GLONASS data processing to derive precise fixed solutions. Consequently, the accuracy of the IFB calibration affects ambiguity fixing, and low-accuracy IFB values in the calibration will degrade the positioning results. Hitherto, at least two IFB rate value sets for various receiver brands have been given in previous studies. Some of the differences between the value sets exceed 2 mm/FN (frequency number) and the effects of those differences on ambiguity fixing have not been investigated until now. This study showed that ambiguity fixing in GLONASS single-epoch positioning is very sensitive to the accuracy of the IFB rates. Even errors of millimetres can seriously lower the empirical success rate. The short baselines from the Global Navigation Satellite System (GNSS, which includes Russian GLONASS) networks of the International GNSS Service and the Regional Reference Frame Sub-Commission for Europe were employed to obtain more accurate IFB rate estimates with the proposed two-dimensional particle filtering. Afterwards, the IFB estimates were statistically refined by the least-squares method. Experiments showed that when the refined IFB rates were used in the IFB calibration, the empirical success rates of ambiguity fixing in GLONASS single-epoch positioning were largely improved compared with the values given before, improvements such as 20·6% for a Septentrio and Leica receiver combination.

Global satellite navigation systems (GNSS) are nowadays used in many applications. GNSS receivers experience limitations in receiving weak signals in a degraded environment. Hence, tracking weak GNSS signals is a topic of interest to researchers in this field. Different methods have been proposed to address this issue, each of which has advantages and disadvantages. In this paper, a method based on the vector tracking method is proposed for weak signal tracking. This method has been developed based on a strong Kalman filter instead of the extended Kalman filter used in conventional vector tracking methods. In order to adjust important parameters of this filter, the fuzzy method is used. The results of tests performed with both simulated data and real data demonstrate that the proposed method performs better than previous ones in weak signal tracking.

The fingerprint positioning (FP) algorithm has been investigated extensively owing to the fact that it can provide a relatively ideal indoor positioning result. However, the effectiveness of the fingerprint algorithm relies on the size of fingerprint database, which prevents the algorithm from being widely applied in practical applications. In this paper, an improved fingerprint algorithm with access point (AP) selection strategy and reference point (RP) selection strategy is proposed to reduce the size of the fingerprint database and improve the positioning accuracy. The experimental results show that the proposed algorithm can reduce the storage size of the fingerprint database by more than 42·64%. Moreover, compared with the FP algorithm, the fingerprint algorithm with segment characteristic distance (FP-SCD) and the fingerprint algorithm with RP selection strategy (FP-RPSS), the average positioning error of the proposed algorithm is reduced by 20·15%, 10·83% and 11·57%, respectively. Therefore, the proposed algorithm has a good application in real positioning scenarios.

Considering that traditional visual navigation cannot be utilised in low illumination and sparse feature environments, a novel visual-inertial integrated navigation method using a Structured Light Visual (SLV) sensor for Micro Aerial Vehicles (MAVs) is proposed in this paper. First, the measurement model based on an SLV sensor is studied and built. Then, using the state model based on error equations of an Inertial Navigation System (INS), the measurement model based on the error of the relative motion measured by INS and SLV is built. Considering that the measurements in this paper are mainly related to the position and attitude information of the present moment, the state error accumulation in traditional visual-inertial navigation can be avoided. An Adaptive Sage-Husa Kalman Filter (ASHKF) based on multiple weighting factors is proposed and designed to make full use of the SLV measurements. The results of the simulation and the experiment based on real flight data indicate that high accuracy position and attitude estimations can be obtained with the help of the algorithm proposed in this paper.

The geometric distribution of navigational aids is one of the most important elements to be taken into account in the planning of maritime terrestrial navigation systems. It determines to a large extent the capability of vessels to obtain high-precision position coordinates. Therefore, the optimisation of their location is a key element at the planning stage, in particular on port approach fairways. This article attempts to use computer simulation methods to assess the positioning accuracy of a vessel that follows a constant course and speed on a port approach fairway. The analysis uses a technique based on the Extended Kalman Filter (EKF) Two-Dimensional (2D) Range-Bearing Simultaneous Location and Mapping (SLAM) method. In the simulation experiment conducted, the research object determined its position based on bearing and distance to fixed position beacons, which changed their locations in subsequent passages of the vessel. A geometrically optimal configuration of the terrestrial navigation marking system guaranteeing the highest positioning accuracy was identified as a result of the deliberations. The study analysed more than 20,000 cases of different configurations (locations) of the fixed position beacons, demonstrating that the adopted algorithm can be used successfully in the planning of their deployment in the context of ensuring minimum accuracy requirements for the positioning of navigational signs on port approach fairways and under restricted conditions by navigational marking services, as set out in International Maritime Organization (IMO) Resolutions A915 (21) and A953(22).

Past research showed that traditional assessment methods that required seafarer students to construct responses based on memorisation and analysing information presented in absence of real-world contexts (e.g. oral examinations and multiple-choice questions) disengaged the students from learning. Memorising information is a lower-order cognitive ability, failure in which led to errors and low academic achievement for students. Authentic assessment methods require students to construct responses through the critical analysis of information presented in real-world contexts. Hence, this research investigated the difference in seafarer students' academic achievement (measured through scores obtained in assessment) in authentic assessment as compared with traditional assessment. Two separate and independent student groups (the ‘control’ group and ‘treatment’ group) were used for a selected unit of learning delivered at the Australian Maritime College within the Bachelor of Nautical Science degree program. Because some past researchers had defined and implemented traditional assessment methods as a single-occasion assessment, this project implemented the assessment in a summative format, as opposed to authentic assessments implemented during student preparation. Analysis of student scores revealed that the authentically assessed students were guided towards significantly higher academic achievement.

In this paper, a new scene recognition visual descriptor called Enhanced Scale Invariant Feature Transform-based Sparse coding Spatial Pyramid Matching (Enhanced SIFT-ScSPM) descriptor is proposed by combining a Bag of Words (BOW)-based visual descriptor (SIFT-ScSPM) and Gist-based descriptors (Enhanced Gist-Enhanced multichannel Gist (Enhanced mGist)). Indoor scene classification is carried out by multi-class linear and non-linear Support Vector Machine (SVM) classifiers. Feature extraction methodology and critical review of several visual descriptors used for indoor scene recognition in terms of experimental perspectives have been discussed in this paper. An empirical study is conducted on the Massachusetts Institute of Technology (MIT) 67 indoor scene classification data set and assessed the classification accuracy of state-of-the-art visual descriptors and the proposed Enhanced mGist, Speeded Up Robust Features-Spatial Pyramid Matching (SURF-SPM) and Enhanced SIFT-ScSPM visual descriptors. Experimental results show that the proposed Enhanced SIFT-ScSPM visual descriptor performs better with higher classification rate, precision, recall and area under the Receiver Operating Characteristic (ROC) curve values with respect to the state-of-the-art and the proposed Enhanced mGist and SURF-SPM visual descriptors.

To detect the abnormal behaviour of ships in the waters of any jurisdiction and to improve the safety of maritime navigation, the meshing-based method is adopted to obtain discrete trajectory data and a probabilistic directed graph model is established to obtain historical data from ships' AIS (automatic identification systems). The state statistical characteristics of each node in the ship probability map are obtained to detect the navigation state of the ship in real time. By predicting the normal navigation trajectory of the ship, it can be judged whether the ship has the potential to behave abnormally at some moment in the future. Simulation experiments were designed based on a maritime simulator platform. The experimental results indicate that the model can correctly predict abnormal behaviour by ships, including excessive speed and deviation from the channel or normal sailing mode.

This paper presents a comparative cyber security resilience estimation of shipboard radars that are implemented on two oil/chemical tankers certified as SOLAS ships. The estimated radars were chosen from the same manufacturer, but belonged to different generations. The estimation was conducted by means of ships' crew interviews and computational testing of the radars using a widely deployed vulnerability scanning software tool. The identified cyber threats were analysed qualitatively in order to gain a holistic understanding of cyber risks threatening shipboard radar systems. The results obtained experimentally indicate that potential cyber threats mainly relate to maintenance of the radars' underlying operating system, suggesting the need for regulatory standardisation of periodic cyber security testing of radar systems.

Global navigation satellite system (GNSS) positioning in dense urban areas remains a challenge due to the signal reflection by buildings, namely multipath and non-line-of-sight (NLOS) reception. These effects degrade the performance of low-cost GNSS receivers such as in those smartphones. An effective three-dimensional (3D) mapping aided GNSS positioning method is proposed to correct the NLOS error. Instead of applying ray-tracing simulation, the signal reflection points are detected based on a skyplot with the surrounding building boundaries. The measurements of the direct and reflected signals can thus be simulated and further used to determine the user's position based on the measurement likelihood between real measurements. Verified with real experiments, the proposed algorithm is able to reduce the computational load greatly while maintaining a positioning accuracy within 10 metres of error in dense urban environments, compared with the conventional method of ray-tracing based NLOS corrected positioning.

The increase in the number of commercial flights highlights the need for air traffic to follow air procedures. Unfortunately, general aviation aircraft used for recreational purposes keep entering controlled and restricted airspace without obtaining permission from air traffic services. Given the safety and operational problems this could potentially cause, this paper examines the underlying reasons for these incidents occurring. In particular, it analyses airspace infringements between 2008 and 2017 involving general aviation flights that were recorded in airspace in which a large number of commercial flights also fly in Europe and America. The reports were analysed based on an initial assessment of their quality. Information was latent in the narrative and subsequently both qualitative (content analysis) and quantitative methods (descriptive statistics) of analysis were used. The analysis revealed that airspace infringements were related to the pilot's flight planning, that is, flight-route choice, navigation skills and communication, in addition to requirement to adhere to airspace procedure. The findings could be used by national authorities and flying clubs to promote safe flying in these regions.

During pulsar navigation, the high-frequency noise carried by the pulsar profile signal reduces the accuracy of the pulse TOA (Time of Arrival) estimation. At present, the main method to remove signal noise by using wavelet transform is to redesign the function of the threshold and level of wavelet transform. However, the signal-to-noise ratio and other indicators of the filtered signal need to be further optimised, so a more appropriate wavelet basis needs to be designed. This paper proposes a wavelet basis design method based on frequency domain analysis to improve the denoising effect of pulsar signals. This method first analyses the pulsar contour signal in the frequency domain and then designs a Crab pulsar wavelet basis (CPn, where n represents the wavelet basis length) based on its frequency domain characteristics. In order to improve the real-time performance of the algorithm, a wavelet lifting scheme is implemented. Through simulation, this method analyses the pulsar contour signal data at home and abroad. Results show the signal-to-noise ratio can be increased by 4 dB, the mean square error is reduced by 61% and the peak error is reduced by 45%. Therefore, this method has better filtering effect.