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Automatic identification system (AIS) data, as a fundamental source of information, plays a very important role in monitoring vessel activity in a maritime surveillance system. However, the behavior of vessels is difficult to recognize. How to take full advantage of available AIS data to discover vessel maneuvering patterns to provide decision support for collision avoidance or complex event dealing has attracted much attention in recent years. Trajectory analysis from AIS sources is an essential branch of the study. There are four levels concerning AIS research: data level, method level, knowledge level and decision level.
Regarding the data level, there are data cleaning, data compression, interpolation or time alignment, data quality, data de-noising and so on. For example, Wei et al. (2020) designed a novel algorithm considering the spatial and motion features of trajectories to compress AIS trajectories based on ship behavior characteristics. Tang et al. (2021) proposed an ADP (Adaptive-threshold Douglas-Peucker) algorithm based on DP (Douglas-Peucker) algorithm to determine the key points of each trajectory through the threshold change rate for ship trajectory compression. Li et al. (2020) used U-Net convolutional networks to construct AIS-based vessel trajectories to obtain the rich skip connections in the network and make great use of historical trajectories. Guo et al. (2021) presented an improved kinematic interpolation for AIS trajectory reconstruction, which integrated data preprocessing and interpolation that considered the ships' kinematic information. With automatic ship reporting systems, Greidanus et al. (2016) discussed how to complete a wide-area maritime situational picture. To combine different data processing methods, Zhang et al. (2016) proposed a new scheme for implementing the Douglas-Peucker (DP) algorithm to complete simplification of AIS trajectories and presented a new AIS-based minimum ship domain evaluation method for simplification threshold determination.
At the method level, there are clustering methods, machine learning methods, statistical methods and so on. For example, Wang et al. (2021) proposed a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to provide insightful knowledge for traffic management and operation optimization. Burger et al. (2020) compared the performance of the Discrete Kalman filter (DKF) and the Linear Regression Model (LRM) to conclude that LRM is a computationally simpler method for trajectory prediction. Jadidi (2021) presented an enhanced density-based spatial clustering of applications with noise (DBSCAN) method to model vessel behaviours based on trajectory point data.