Collisions are one of the major accidents in the shipping industry, causing significant losses. In this work, a framework of marine collision risk identification strategy was developed to quantitatively analyse collision risks and provide an easy and convenient way to monitor traffic flow in relevant waters to mitigate the chances of collision. The model was verified by using automatic identification system data obtained from Tianjin Port. When compared to previous research, the proposed model can identify risks earlier and give people more time to analyse and take action. The results indicate that it also can provide a visual display to alert relevant personnel. The model can be used as a reference to identify potential collision risks or as an information source for future research.

A relatively novel approach of autonomous navigation employing platform dynamics as the primary process model raises new implementational challenges. These are related to: (i) potential numerical instabilities during longer flights; (ii) the quality of model self-calibration and its applicability to different flights; (iii) the establishment of a global estimation methodology when handling different initialisation flight phases; and (iv) the possibility of reducing computational load through model simplification. We propose a unified strategy for handling different flight phases with a combination of factorisation and a partial Schmidt–Kalman approach. We then investigate the stability of the in-air initialisation and the suitability of reusing pre-calibrated model parameters with their correlations. Without GNSS updates, we suggest setting a subset of the state vector as ‘considered’ states within the filter to remove their estimation from the remaining observations. We support all propositions with new empirical evidence: first in model-parameter self-calibration via optimal smoothing and second through applying our methods on three test flights with dissimilar durations and geometries. Our experiments demonstrate a significant improvement in autonomous navigation quality for twelve different scenarios.

This paper proposes a proper compass adjustment method using only a GPS (or any other GNSS receiver) and a single visual reference to enhance the efficiency of compass adjustment. During compass adjustment, the ship proceeds on magnetic courses using a gyroscopic or satellite compass and considering magnetic declination. However, non-magnetic compasses are only compulsory for ships of 500 gross tonnage or upwards (SOLAS V/19.2.5.1). Many ships of less than 500 gross tonnage have only a magnetic compass to indicate heading. In these cases, a minimum of five leading lines or a minimum of five bearings of conspicuous and distant points or sun azimuths are necessary to adjust the compass. This makes compass adjustment more laborious and time consuming. To expedite this process, a reliable and practical method was developed to use the courses over ground given by a GNSS receiver and a single visual reference instead of the headings provided by a gyroscopic or satellite compass. The method is valid for all ships, but is primarily intended for those equipped with only a magnetic compass to indicate heading.

Currently, most of the studies on unmanned aerial vehicle (UAV) automatic landing systems mainly depend on image information to determine the landing location. However, the system requires a camera, a gimbal system and a separate image-processing device, which increases the weight and power consumption of the UAV, resulting in a shorter flight time. In addition, a large amount of computation and slow reaction speed can cause the camera to miss a proper landing moment. To solve these problems, in this study, the moving direction and relative distance between an object and the automatic landing system were measured using a receive signal strength indicator of the radio-frequency (RF) signal. To improve the movement direction and relative distance estimation accuracy, the noise in the RF signal was minimised using a low pass filter and moving average filter. Based on the filtered RF signal, the acceleration of the multicopter to reach the object was estimated by adopting the proportional navigation algorithm. The performance of the proposed algorithm for precise landing on a moving vehicle was demonstrated through experiments.

Thanks to the development of the real-time kinematic (RTK) algorithm and the emerging Global Navigation Satellite System (GNSS), especially for Galileo and BeiDou-3, reliable positioning accuracy for medium and long-baseline RTK became possible globally. Moreover, with the development of the GNSS receiver hardware, baseline length limitations due to radio-based communications are removed thanks to internet-based communication. In this work, single-baseline RTK, incorporated partial ambiguity resolution with troposphere and ionosphere weighting, using GPS (G), Galileo (E), BeiDou-3 (C3) and multi-GNSS (GE and GEC3), is conducted with real GNSS data of EUREF Permanent GNSS network under three different cutoff angles (10°, 20°, and 30°) for six different lengths of baselines (~50, ~150, ~250, ~350, ~450, and ~550 km). The results show that the multi-GNSS RTK solution significantly contributed to the positioning accuracy and convergence time of the single-system RTK solutions. Based on the results, non-available epoch-wise solutions for the high-degree cutoff angles are more obvious for the single-system RTK, whereas multi-GNSS solutions provide 100% solutions for each cutoff angle and baseline. The results indicate that instantaneous and a few epochs single-epoch ambiguity resolution is feasible for 50, 150, 250 and 350 km baseline lengths for multi-GNSS RTK. Based on the positioning results, horizontal–vertical positioning improvements of multi-GNSS RTK (GEC3) compared with the single-system GPS RTK are found as 50%–37%, 40%–35%, 55%–47%, 53%–54%, 57%–49% and 57%–49% for 50, 150, 250, 350, 450 and 550 km, respectively, under a 10° cutoff angle. For 20° and 30° cutoff angles, the accuracy improvements are much higher. The convergence time improvements (n/e/u) of multi-GNSS RTK (GEC3) compared with the single-system GPS RTK are found as 86/92/75%, 77/67/72%, 75/77/83%, 53/56/52%, 69/49/62%, and 52/45/39% for 50, 150, 250, 350, 450 and 550 km, respectively, under a 10° cutoff angle.

This paper introduces a novel approach for extracting vessel navigation patterns from very large automatic identification system (AIS) trajectory big data. AIS trajectory data records are first converted to a series of code documents using vector quantisation, such as k-means and PQk-means algorithms, whose performance is evaluated in terms of precision and computational time. Therefore, a topic model is applied to these code documents from which vessels’ navigation patterns are extracted and identified. The potential of the proposed approach is illustrated by several experiments conducted with a practical AIS dataset in a region of North West France. These experimental results show that the proposed approach is highly appropriate for mining AIS trajectory big data and outperforms common DBSCAN algorithms and Gaussian mixture models.