###### Abstract
However, these systems have shortcomings in terms of insufficient perception means, insufficient accuracy, real-time performance, single mutual communication information, and high demands on the operator's ability to interpret and understand the information, which make it difficult to meet the requirements of efficient navigation assistance in complex waterways. Therefore, many scholars have actively pursued strategies for integrating multiple perception methods, such as radar, sonar, Synthetic Aperture Radar (SAR), remote sensing, cameras, etc. [9; 10; 11; 12], in order to comprehensively and accurately acquire information about the navigation environment. However, an effective and intelligent data fusion mechanism has not yet been proposed in the existing research, and at the same time, the method of expressing and understanding the sensing information is more demanding for the crew. Specifically, for the perception of ship target positions, existing research predominantly focuses on methods utilizing shore-based fixed-view cameras to project AIS position coordinates onto image coordinates for matching and fusion. However, this approach fails to address the issue of matching and fusion for ships with a deactivated AIS, as their specific position coordinates cannot be obtained [13]. Furthermore, current multi-source perception data fusion methods primarily concentrate on the relationship between target image positions and AIS positions at a single moment [14], neglecting the long-term trajectory characteristics of ships. Consequently, in busy waters with high ship density or when AIS signals are subject to interference, ensuring high-quality association and fusion of perception data becomes challenging. Additionally, the expression and interpretation methods of perceived information in existing navigation assistance systems impose high cognitive demands on crew members [15], potentially leading to decision-making errors. To address these challenges, in this study, a perception data fusion framework based on ship spatio-temporal trajectory features (ST-TF) was proposed. This framework was designed for application in ship AR navigation assistance systems, offering a more reliable and efficient solution for navigation and surveillance in complex waterways. The main contributions of this paper are summarized as follows:  
* Fusion Location Ship (FLShip) dataset: The FLShip dataset has been constructed based on data collected from six self-developed MASSs, which includes stereo and monocular image sequences, as well as high-frequency attitude and GPS data from the MASSs. A variety of navigational scenarios and environmental conditions are encompassed by the dataset, providing essential support for ship target detection, multi-source information fusion, and autonomous navigation research. * Optimization of ship tracking system: To improve the detection performance of small targets while reducing the model's parameter count, a Bi-YOLO network based on BiFomer has been proposed, achieving more efficient and accurate ship identification. In response to complex navigational environments, the more powerful OC-SORT algorithm has been incorporated to ensure stable tracking of targets. The superiority of this method has been validated through comparative experiments with several advanced detection and tracking algorithms. * Ship target absolute position estimation: In order to realize the position estimation of the target ship in the state of no reference, a visual localization model based on binocular imaging was constructed. By fusing the attitude information from a binocular camera, absolute position estimation of maritime targets at the moving end of a ship is successfully realized. * Perception data fusion based on ship ST-TF: To effectively integrate information from multiple perception sources, a novel perception data fusion method based on ship ST-TF was proposed.