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.

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.

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.

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.

A spoofing attack on a global navigation satellite system (GNSS) receiver is a threat to a significant community of GNSS users due to the high stakes involved. This paper investigates the use of slope based metrics for the detection of spoofing. The formulation of slope based metrics involves monitoring correlators along with tracking correlators in the receiver's channel, which are slaved to the prompt tracking correlator. In this study, using some candidate metrics, detectors have been formed through the analysis of simulated spoofing attacks. A theoretical variance of each metric has also been calculated as a reference for the threshold. A threshold is estimated using the measured variance from the clean signals, for specific false alarm rate. By using the measured threshold, detectors are formed based on slope metrics. These detectors have been tested using TEXBAT data. The results show that the differential slope metrics have good performance. The results have also been compared with some other techniques of spoofing detection.

Considering unobserved spatial-temporal interaction effects, this study proposes a Bayesian spatial-temporal interaction model for predicting economic loss from fishing boat collisions using 10-year (2004–2014) collision records from six different areas in the waters of Fujian, China. Results show strong spatial heterogeneity and correlation effects in fishing boat collisions, while the economic loss from boat collisions gradually decreases with the time trend. Collision time, collision location, visibility and the involvement of LNG/LPG/chemical-carrying ships show similar marginal effects on the economic loss for the two collision types: fishing boat collisions and collisions involving no fishing boats. Navigational status and the involvement of cargo ships exhibit much bigger effects in fishing boat collisions compared with collisions involving no fishing boat. Unlike collisions involving no fishing boat, fishing boat collisions are associated with reduced economic loss in poor weather conditions characterised by strong wind/waves because in Fujian waters additional safety measures are adopted for fishing boats in such conditions. The proposed model is useful for policymakers in adopting safety enhancement strategies to decrease the economic loss resulting from fishing boat collisions.

This paper describes a method to determine global navigation satellite systems (GNSS) satellite orbits and clocks for advanced receiver autonomous integrity monitoring (ARAIM). The orbit and clock estimates will be used as a reference truth to monitor signal-in-space integrity parameters of the ARAIM integrity support message (ISM). Unlike publicly available orbit and clock products, which aim to maximise estimation accuracy, a straightforward and transparent approach is employed to facilitate integrity evaluation. The proposed monitor is comprised of a worldwide network of sparsely distributed reference stations and will employ parametric satellite orbit models. Two separate analyses, covariance analysis and model fidelity evaluation, are carried out to assess the impact of measurement errors and orbit model uncertainty on the estimated orbits and clocks, respectively. The results indicate that a standard deviation of 30 cm can be achieved for the estimated orbit/clock error, which is adequate for ISM validation.

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Wi-Fi based indoor localisation has become attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which can be used for Wi-Fi ranging between intelligent terminal and Wi-Fi access point. This paper introduces a framework of Wi-Fi FTM data acquisition and processing that can be used for indoor localisation. We analyse the main factors that affect the accuracy of Wi-Fi ranging and propose a calibration, filtering and modelling algorithm that can effectively reduce the ranging error caused by clock deviation, non-line-of-sight (NLOS) and multipath propagation. Experimental results show that the proposed calibration and filtering method is able to achieve metre-level ranging accuracy in case of line-of-sight by using large bandwidth. Estimation results also show that the proposed Wi-Fi ranging model provides an accurate ranging performance in NLOS and multipath contained indoor environment; the final positioning error is less than 2·2 m with a stable output frequency of 3 Hz.

With the development of sensor technology, sensor nodes are increasingly being used in underwater environments. The strategy presented in this paper is designed to solve the problem of using a limited number of autonomous underwater vehicles (AUVs) to complete tasks such as data collection from sensor nodes when the number of AUVs is less than the number of target sensors. A novel classified self-organising map algorithm is proposed to solve the problem. First, according to the K-means algorithm, targets are classified into groups that are determined by the number of AUVs. Second, according to the self-organising map algorithm, AUVs are matched with groups. Third, each AUV is provided with the accessible order of the targets in the group. The novel classified self-organising map algorithm can be used not only to reduce the total energy consumption in a multi-AUV system, but also to give the most efficient accessible order of targets for AUVs. Results of simulations conducted to prove the applicability of the algorithm are given.

Naval maritime operations entail a permanent concern for safety, ensuring that all crew members receive the necessary information on time. This implies the existence of specific training for improving non-technical skills (NTS). This paper proposes that bridge resource management (BRM) may be determinant for the success of naval maritime operations. Through a literature review on NTS, maritime team training and BRM, the paper presents insights about the way the level of NTS, inherent to BRM, may be determinant for naval officers to operate in safety. We propose that human error may be minimised and safety maximised in military teams operating in the maritime environment through the implementation of an NTS training programme. The paper offers an insight into the importance of safety during maritime operations, focusing on recent international orientations about training requirements, proposing that implementing BRM will be pivotal for the future of the military navy context.

The lane-level map, which contains the lane-level information severely lacking in widely used commercial navigation maps, has become an essential data source for autonomous driving systems. The linking relations between lane-level map and commercial navigation map can facilitate an autonomous driving system mapping information between different applications using different maps. In this paper, an approach is proposed to build the linking relations automatically. The different topology networks are first reconstructed into similar structures. Then, to build the linking relations automatically, the adaptive multi-filter algorithm and forward path exploring algorithm are proposed to detect corresponding junctions and paths, respectively. The approach is validated by two real data sets of more than 150 km of roads, mainly highway. The linking relations for nearly 94% of the total road length have been built successfully.