## 1 Introduction
### State of the Art  
The problem of tracking vessel movements has gained importance in recent years. This is due to the increasing capabilities for recording and analyzing vessel behavior based on historical data. Real-time tracking techniques are also advancing. Currently, the most effective real-time system is arguably the British \"Nordic Warden\", which successfully monitored the actions of a Russian scientific submarine [2]. Artificial intelligence methods are commonly used for ship tracking. Various deep learning techniques for vessel trajectory prediction are widely applied in research and are presented, among others, in [3; 4]. Pre-trained YOLO object detection algorithms are also commonly employed. Applications of different versions of the YOLO algorithm (You Only Look Once) for object recognition and tracking are described in [5; 6; 7; 8]. The \"cascade vector\" tracking method was presented by [PERSON] et al. [9]. [PERSON] et al. proposed a reliable UAV-based multi-ship tracking method [10]. Novel approaches for tracking autonomous vessels were presented by [PERSON] et al. and [PERSON] et al. [11; 12; 13]. [PERSON] et al. developed an adaptive FairMOT tracking method applicable to maritime targets [14]. FairMOT is a real-time multi-object tracking method. It is based on a neural network and performs simultaneous object tracking and identification. The method uses the CenterNet architecture with Deep Layer Aggregation (DLA). CenterNet detects objects by locating their centers, while DLA enables better fusion of information from different levels of the neural network, which improves the accuracy and efficiency of the model. ADP-based adaptive control for course tracking with prescribed performance was proposed in [15]. ADP is an emerging advanced control technology developed for nonlinear dynamical systems and based on the idea of approximating dynamic programming solutions. [PERSON] et al. introduced an enhanced SiamMask network for coastal ship tracking [16]. A Siamese network is a deep learning architecture designed to compare two inputs by processing them through identical neural networks and measuring their similarity. The SiamMask architecture consists of two Siamese networks. Working in parallel, one is acting as a template and the other as a detection branch. A memory-guided perceptual network for coastal ship tracking was proposed in [17]. [PERSON] presented a contemporary architecture of Global Ship Tracking (GST) systems, including networks and equipment [18]. A novel course-tracking control method for ships with input delay was described in [19]. The feasibility of ship detection and tracking using GOCI-II satellite images was demonstrated by [PERSON] et al. [20]. The Geostationary Ocean Color Imager may be considered as an alternative for AIS tracking. A method based on deep reinforcement learning for ship collision avoidance and path tracking was presented in [21]. [PERSON] et al. introduced a robust online learning approach based on a Siamese network for ship tracking [22]. [PERSON] et al. developed a ship trajectory extraction method using a Python-based interpolation technique [23]. Ship tracking using distributed acoustic sensing data was explored in [24]. The multi-criteria decision analysis method is outlined in [25], with an example of its application in the maritime domain provided in [26]. The authors applied decision-making analysis to evaluate and rank selection criteria and vehicle types for search and rescue operations at sea. [PERSON] et al. [27] presented an approach to detecting anomalies in vessel movement that is similar to the one proposed by the authors of the present work. AIS data was also used in their analysis, appropriately filtered, clustered, and then processed using clustering and a Deep Recurrent Neural Network. A similar approach, combining clustering and movement prediction, was proposed by [PERSON] et al. in [28]. Both references [27; 28] are based on trajectory prediction using neural networks, which also serves as a means of anomaly detection.