## 2 Literature Review
### Multi-Sensor Data Fusion  
To obtain more accurate and consistent ship motion perception data, multi-source data fusion methods have become a focus of attention in the academic community [38,39]. Maritime radars, AISs, and cameras are the most widely used sensors in maritime transportation systems, each possessing distinct advantages and limitations. Therefore, effectively integrating data from these sensors to enhance ship detection and tracking accuracy has become a key research focus. In previous studies, scholars have made a lot of effort in the fusion of maritime radars, AISs, and video surveillance cameras. For example, to improve the accuracy of AIS visual target association, [PERSON] et al. proposed a three-step calibration method that projects image coordinates onto geographic coordinates [14]. By employing the Hungarian algorithm, they identified the optimal matches, effectively integrating AIS data with ship trajectories. [PERSON] et al. introduced an image-AIS data fusion approach for fixed and periodically panning cameras [40]. This method utilizes the YOLOv5 model for ship detection, applies homography transformation for coordinate conversion, and assigns AIS messages to the nearest bounding box in the image using k-dimensional tree-based nearest neighbor search. The accuracy of the fusion framework was further validated. To improve ship tracking efficiency, [PERSON] et al. integrated the AIS with maritime video surveillance. They adjusted the camera's attitude and focus based on AIS-reported positions to achieve stable tracking of specific ships [41]. Although this method performs well in single-target scenarios, it faces significant limitations in multi-ship scenarios and cannot achieve efficient multi-target tracking. Additionally, maritime radar images often contain false echoes, and AIS data transmission may experience loss, leading to uncertainties in estimating ship speed and heading. To address this issue, [PERSON] et al. proposed a method that integrates radar sequence images with AIS data for estimating ship speed and heading [42]. This method was applied in the Yangtze River Basin, effectively reducing the uncertainty in ship speed and heading estimation. To improve waterway traffic monitoring in inland waterways, [PERSON] et al. proposed a framework for ship identification through the fusion of visual detection and AIS data [43]. This method estimates the distance from the camera and the azimuth relative to the camera of the identified ship target based on the bounding box size in the shore-based monocular camera image and the ship length in the AIS data. However, this approach is only applicable for the distance estimation of ships with a side-facing orientation to the camera. Moreover, due to the effects of marine background, occlusion, and weather conditions, the bounding box size often varies, further impacting the accuracy of the estimation. For the fusion of ship trajectories, [PERSON] et al. proposed a vision-based framework for fusing ship and AIS trajectories [13] that uses the YOLOX detector and optimized Deep SORT tracker to extract ship image trajectories, preprocesses AIS data, and projects AIS coordinates to the image coordinate system. The Hungarian algorithm then associates AIS and visual data for intelligent maritime traffic monitoring in inland waterways. However, this method does not fully consider the limited computing resources of onboard devices, and the use of original YOLOX may result in slower inference speed. [PERSON] et al. proposed a method for matching AIS and video data by combining region division and an improved Kalman filter [44]. The method utilizes an enhanced LSTM network for accurate trajectory prediction of AIS data and applies a single-stage object detection model to analyze video data collected from buoys. This approach enables fast and efficient data fusion, significantly improving the accuracy and flexibility of channel detection.