Missed detection probability is a critical metric for the integrity performance of receiver autonomous integrity monitoring (RAIM) in the presence of faults. The traditional missed detection probability evaluation method for RAIM is limited by impractical time consumption because of the absence of accurate searching interval for the magnitude of a worst-case fault. To address this issue, the searching interval for the magnitude of a worst-case fault is constructed by the combination of minimum detectable magnitude and minimum hazardous magnitude, and the searching interval adjustment is designed to avoid the absence of worst-case fault magnitude so that the maximum missed detection probability can be accurately evaluated. The simulation result indicates that the proposed method can achieve higher accuracy for the worst-case fault magnitude searching. Furthermore, the accuracy of worldwide evaluated missed detection rate can achieve an improvement of 57·66% at most by the proposed method for the different classical RAIM algorithms.

Aiming at the error estimation problem of a radar detection system when the variation law of system error is unknown, an improved Gaussian mean-shift radar dynamic error registration algorithm (IGMSR) is proposed. The algorithm can effectively adapt to the variation of system error when the variation law of system error is unknown. The IGMSR algorithm uses the mean-shift method to contribute different characteristics to the estimation results of different sample points, and constructs weight coefficients according to the deviation of sample points from the mean and sampling time. The simulation results show that more than 90% of the constant system errors can be eliminated; for the systematic error with slow change, more than 80% of the bias can be eliminated in real time, while a previous method of Zhu and Wang (2018) can only eliminate 60% of the systematic error and require the change law to be known. This method overcomes the influence of random error and abnormal point, and the estimation results are more robust.

The release of GNSS raw data on Android smartphones provides the potential for high-precision smartphone positioning using multi-constellation and multi-frequency signals. However, severe multipath and low observation quality in kinematic environments make double-differenced uncombined ambiguities difficult to resolve reliably. To address this, the paper proposes an improved wide-lane (WL) integer ambiguity resolution (IAR) method that combines integer rounding and the Least-Square AMBiguity Decorrelation Adjustment (LAMBDA) methods. The proposed method achieved fix rates of 57% to 70% in challenging environments, with an average improvement of 7 · 7% in horizontal positioning accuracy compared to the float solution. The traditional partial integer rounding method only improved accuracy by 1 · 1%.

In Global Navigation Satellite Systems (GNSS)-denied environments, aiding a vehicle's inertial navigation system (INS) is crucial to reducing the accumulated navigation drift caused by sensor errors (e.g. bias and noise). One potential solution is to use measurements of gravity as an aiding source. The measurements are matched to a geo-referenced map of Earth's gravity to estimate the vehicle's position. In this paper, we propose a novel formulation of the map matching problem using a hidden Markov model (HMM). Specifically, we treat the spatial cells of the map as the hidden states of the HMM and present a Viterbi style algorithm to estimate the most likely sequence of states, i.e. most likely sequence of vehicle positions, that results in the sequence of observed gravity measurements. Using a realistic gravity map, we demonstrate the accuracy of our Viterbi map matching algorithm in a navigation scenario and illustrate its robustness compared with existing methods.

By simultaneously estimating satellite clock drifts (SCDs) as either constant parameters or piece-wise parameters, we present an improved integrated orbit determination and time synchronization approach for BDS-3 satellites with raw inter-satellite link (ISL) observations. Experiments with L-band data from seven monitoring stations in China and ISL data from eight satellites of the third-generation Beidou Navigation Satellite System (BDS-3) were carried out and the two SCD estimation strategies are validated. It is demonstrated that, with SCDs estimated, the quality of orbits and clock offsets is comparable to those with SCDs corrected using predicted values. The accuracy of the estimated orbits and clocks are up to 0.019 m (radial) and 0.095 ns, respectively, with improvements of 95% and 90%, when compared with the results using the L-band data alone. It is also demonstrated that estimating SCDs time slice by time slice is slightly worse in accuracy but superior in coping with possible frequency jump of satellite clocks.

To realise the overall calibration of the error model coefficients of accelerometers in an inertial combination and to improve the navigation accuracy of the inertial navigation system, a norm-observation method is applied to the calibration, especially for the quadratic coefficient of the accelerometer. The Taylor formula is used to expand the solution of the acceleration model, and the intermediate variables with error model coefficients are obtained using the least square method. The formulas for calculating the quadratic term coefficient, scale factor and bias of the accelerometer are given. A 20-position method is designed to calibrate the accelerometer combination, the effectiveness of the method is verified by simulation, and the effects of installation misalignment and rod-arm error on calibration accuracy are analysed. The results show that the installation misalignments and rod-arm errors have little influence on the coefficient calibration, less than 10−8, and can be neglected in a practical calibration process.

In the contemporary maritime industry, characterised by intense competition, reduced visibility due to heavy fog is a primary cause of accidents, significantly impairing maritime operational efficiency. Consequently, investigating foggy weather navigation safety holds crucial practical significance. This paper, through an analysis and synthesis of various aspects of foggy navigation technology, including foggy navigation regulations at different ports, fog warnings, foggy vessel environmental perception and foggy auxiliary navigation systems, explores the key issues concerning vessel navigation during foggy conditions from a scientific perspective. This discussion encompasses the aspects of regulatory frameworks, standardisation, and the development of intelligent and responsive onboard equipment. Finally, the paper offers a glimpse into potential strategies for fog navigation.

In this paper, a complete introduction to the dead reckoning navigation technique is offered after a discussion of the many forms of navigation, and the benefits and drawbacks associated with each of those types of navigation. After that, the dead reckoning navigation solution is used as an option that is both low-cost and makes use of the sophisticated equations that are used by the system. Moreover, to achieve the highest level of accuracy in navigation, an investigation of navigation errors caused by dead reckoning is calculated. Employing the suggested dead reckoning navigation system, the final position of an underwater vehicle can be established with a high degree of accuracy by using experimental data (from sensors) and the uncertainties that are associated with the system. Finally, to illustrate the correctness of the dead reckoning navigation process, the system error analysis as uncertainty that was carried out using experimental data using the dead reckoning navigation technique is compared with GPS data.

The identification of fishing vessel operations holds significant importance in addressing fishing industry issues, such as overfishing and illegal, unreported and unregulated fishing (IUUF). Many countries utilise data from vessel monitoring systems (VMSs) or automatic identification systems (AISs) to monitor fishing activities. These data include vessel trajectories, headings and speeds, among others. We aimed to analyse the fishing behaviours of three types of fishing gear used by vessels (trawl, purse seine and gill net) and identify the types of gear employed by the vessels. Therefore, a 1D CNN-LSTM fishing vessel operational behaviour prediction model was proposed by combining a one-dimensional convolutional (1D CNN) neural network and a long short-term memory (LSTM) neural network. The model utilises 1D CNN to extract local features from fishing vessel trajectories and employs LSTM to capture the time series information in the data, eventually classifying fishing gears. The results show that the proposed model achieves a classification accuracy of 92% in categorising fishing vessel operational trajectories. This study significantly contributes to preventing IUUF, curtailing overfishing, and enhancing fisheries management strategies.

The International Regulations for the Prevention of Collisions at Sea (IRPCS) provide a comprehensive set of instructions for watchkeeping officers to follow and prevent collisions at sea. This study compares how six newly qualified deck officers and six Master Mariners, who were all trained at the same college, applied the IRPCS. Individual, semi-structured interviews were used to uncover how the 12 participants applied and interpreted the rules for three authentic scenarios. Phenomenography was used to capture the qualitatively different means by which participants interpreted the IRPCS. For basic collision avoidance situations, the results indicated little difference between the cohorts' ability to interpret and apply the IRPCS. However, when the scenarios became more complicated, Master Mariners outperformed newly qualified deck officers. In these cases, Master Mariners displayed a greater capacity to assess the overall situation, whereas newly qualified deck officers tended to simplify by focusing on a single rule. These findings indicate that training needs to focus on developing situational awareness; and training scenarios need to incorporate multiple vessels in authentic scenarios to enhance newly qualified deck officers' capacities to interpret the IRPCS.

Vessel trajectories from the Automatic Identification System (AIS) play an important role in maritime traffic management, but a drawback is the huge amount of memory occupation which thus results in a low speed of data acquisition in maritime applications due to a large number of scattered data. This paper proposes a novel online vessel trajectory compression method based on the Improved Open Window (IOPW) algorithm. The proposed method compresses vessel trajectory instantly according to vessel coordinates along with a timestamp driven by the AIS data. In particular, we adopt the weighted Euclidean distance (WED), fusing the perpendicular Euclidean distance (PED) and synchronous Euclidean distance (SED) in IOPW to improve the robustness. The realistic AIS-based vessel trajectories are used to illustrate the proposed model by comparing it with five traditional trajectory compression methods. The experimental results reveal that the proposed method could effectively maintain the important trajectory features and significantly reduce the rate of distance loss during the online compression of vessel trajectories.

In previous research, several computational methods have been proposed to analyse the navigation, transportation safety and collision risks of maritime vessels. The objective of this study is to use Automatic Identification System (AIS) data to assess the collision risk between two vessels before an actual collision occurs. We introduce the concept of an angle interval in the model to enable real-time response to vessel collision risks. When predicting collision risks, we consider factors such as relative distance, relative velocity and phase between the vessels. Lastly, the collision risk is divided into different regions and represented by different colours. The green region represents a low-risk area, the yellow region serves as a cautionary zone and the red region indicates a high-alert zone. If a signal enters the red region, the vessel's control system will automatically intervene and initiate evasive manoeuvres. This reactive mechanism enhances the safety of vessel operations, ensuring the implementation of effective collision avoidance measures.

Wall-climbing robots work on large steel components with magnets, which limits the use of wireless sensors and magnetometers. This study aims to propose a novel autonomous localisation method (RGBD-IMU-AL) with an inertial measurement unit and a fixed RGB-D camera to improve the localisation performance of wall-climbing robots. The method contains five modules: calibration, tracking, three-dimensional (3D) reconstruction, location and attitude estimation. The calibration module is used to obtain the initial attitude angle. The tracking and 3D reconstruction module are used jointly to obtain the rough position and normal vector of the robot chassis. For the location module, a normal vector projection method is established to screen out the top point on the robot shell. An extended Kalman filter (EKF) is used to estimate the heading angle in the attitude estimation module. Experimental results show that the positioning error is within 0⋅02 m, and the positioning performance is better than that of the MS3D method. The heading angle error remains within 3⋅1°. The obtained results prove its applicability for the autonomous localisation in low-texture and magnetically disturbed environments.

Official interpretations of Doppler shifts from the final satellite communications of missing Malaysian Airlines MH370 were based on a motion-decoupled ‘Up-Down model’. That model predicted an uncontrolled high-speed gravitationally accelerated dive following fuel starvation. Here, I challenge that model using a more-realistic motion-coupled ‘Declination model’. Aerial, satellite and underwater searches failed to find the predicted official violent crash-site near the 7th arc. Meticulous re-examination of debris damage by air-crash investigator Larry Vance concluded that the aircraft glide-landed under power with extended wing-flaps. The trailing-edges were then damaged, broke off their mountings, flailing about and retracted along the guides to cause the observed wing-flap damage. Larry's conclusions complement interpretations from the ‘Declination model’ which we demonstrate here with three example flight tracks. Our revised Doppler-shift analyses support the hypothesis of a controlled eastward descent. We conclude that the official theory of fuel starvation and a high-speed dive are fundamentally flawed.

Information is provided to navigators through advanced onboard navigation equipment, such as the electronic chart display and information system (ECDIS), radar and the automatic identification system (AIS). However, maritime accidents still occur, especially in coastal and inland water where many navigational dangers exist. The recent artificial intelligence (AI) technology is actively applied in navigation fields, such as collision avoidance and ship detection. However, utilising the aids to navigation (AtoN) system requires more engagement and further exploration. The AtoN system provides critical navigation information by marking the navigation hazards, such as shallow water areas and wrecks, and visually marking narrow passageways. The prime function of the AtoN can be enhanced by applying AI technology, particularly deep learning technology. With the help of this technology, an algorithm could be constructed to detect AtoN in coastal and inland waters and utilise the detected AtoN to create a safety function to supplement watchkeepers using recent navigation equipment.

We present a deep learning approach for near real-time detection of Global Navigation Satellite System (GNSS) radio frequency interference (RFI) based on a large amount of aircraft data collected onboard from the Global Positioning System (GPS) and Attitude and Heading Reference System (AHRS). Our approach enables detection of GNSS RFI in the absence of total GPS failure, i.e. while the receiver is still able to estimate a position, which means RFI sources with low power or at larger distance can be detected. We demonstrate how deep one-class classification can be used to detect GNSS RFI. Furthermore, thanks to a unique dataset from the Swiss Air Force and Swiss Air-Rescue (Rega), preprocessed by Swiss Air Navigation Services Ltd. (Skyguide), we demonstrate application of deep learning for GNSS RFI detection on real-world large scale aircraft data containing flight recordings impacted by real jamming. The approach we present is highly general and can be used as a foundation for solving various automated decision-making problems based on different types of Communications, Navigation and Surveillance (CNS) and Air Traffic Management (ATM) streaming data. The experimental results indicate that our system successfully detects GNSS RFI with 83$\,\cdot\,$5% accuracy. Extensive empirical studies demonstrate that the proposed method outperforms strong machine learning and rule-based baselines.

Identifying the absence of situation awareness (SA) in air traffic controllers is critical since it directly affects their hazard perception. This study aims to introduce and validate a multimodal methodology employing electroencephalogram (EEG) and eye-tracking to investigate SA variation within specific air traffic control contexts. Data from 28 participants executing the experiment involving three different SA-probe tests illustrated the conceptual relationship between EEG and eye-tracking indicators and SA variations, using behavioural data as a proxy. The results indicated that both EEG and eye-tracking metrics correlated positively with the SA levels required, that is, the frequency spectrum in the β (13–30 Hz) and γ (30–50 Hz) bands, alongside the fixation/saccade-based indicators and pupil dilation increased in response to higher SA levels. This research has substantial implications for investigating SA using a human-centric approach via psychophysiological indicators, revealing the intrinsic interactions between the human capability envelope and SA, contributing to the development of a real-time monitoring system of SA variations for air transportation safety research.

Ship trajectory prediction plays a critical role in collision detection and risk assessment. To enhance prediction accuracy and efficiency, a novel hybrid particle swarm optimisation (PSO) and grey wolf optimisation (GWO) long, short-term memory (LSTM) network model is proposed (PGL model). The hybrid PSO-GWO optimisation method combines the algorithm's strengths and offers improved stability and performance. The hybrid algorithm is employed to optimise the hyperparameters of the LSTM neural networks to enhance prediction accuracy and efficiency. To demonstrate the superiority of the PGL model, the LSTM, PSO-LSTM and PGL are applied to the same dataset, and then prediction performance and processing time are compared. Experimental results indicate that the proposed PGL algorithm outperforms prediction accuracy and optimisation time.

Indoor navigation for micro aerial vehicles (MAVs) is challenging in GPS signal-obstructed indoor corridor environments. Position and heading estimation for a MAV is required to navigate without colliding with obstacles. The connected components algorithm and k-means clustering algorithm have been integrated for line and vanishing point detection in the corridor image frames to estimate the position and heading of the MAV. The position of the vanishing point indicates the position of the MAV (centre, left or right) in the corridor. Furthermore, the Euclidean distance between the image centre and mid-pixel coordinates at the last row of the image and the detected vanishing point pixel coordinates in the successive corridor image frames are used to compute the heading of the MAV. When the MAV deviates from the corridor centre, the position and heading measurement can send a suitable control signal to the MAV and align the MAV at the centre of the corridor. When compared with a grid-based vanishing point detection method heading accuracy of ±1⋅5°, the k-means clustering-based vanishing point detection is suitable for real-time heading measurement for indoor MAVs with an accuracy of ±0⋅5°.

Unmanned aerial vehicles (UAVs) have recently been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, UAVs can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative UAVs will offer diverse interpretations which support a multiperspective view of the scene. Due to the diverse interpretations of UAVs usually deviating, UAVs require a consensus interpretation for the scenario. This study presents an original consensus-based method to pilot multi-UAV systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Taylor series are used to describe the fuzzy nonlinear plant and derive the stability analysis using polynomial functions, which have the representations $V(x )={m_{\textrm{1} \le l \le N}}({{V_\textrm{l}}(x )} )$ and ${V_l}(x )={x^T}{P_l}(x )x$. Due to the fact that the ${\dot{P}_l}(x )$ in ${\dot{V}_l}(x )={\dot{x}^T}{P_l}(x )x + {x^T}{\dot{P}_l}(x )x + {x^T}{P_l}(x )\dot{x}$ will yield intricate terms to ensure a stability criterion, we aim to avoid these kinds of issues by proposing a polynomial homogeneous framework and using Euler's functions for homogeneous systems. First, this method permits each UAV to establish high-level conditions from the probed events via a fuzzy-based aggregation event. The evaluated consensus indicates how suitable is the scenario collective interpretation for every UAV perspective.