| Abstract | Accurate tracking of maritime obstacles is essential to ensuring safe ship navigation. To this end, a variety of sensors—such as AIS (Automatic Identification System), RADAR (RAdio Detection And Ranging), LiDAR (Light Detection And Ranging), and cameras—are widely employed. Data from these sensors is used to estimate the state of surrounding obstacles. However, each sensor has unique characteristics, including its detection range and measurement frequency, making it challenging to achieve stable and precise tracking with a single sensor. Therefore, a sensor fusion method for obstacle tracking is required to compensate for each sensor’s strengths and weaknesses. In this study, we proposed a track-to-track fusion method to improve the tracking accuracy of maritime obstacles by integrating RADAR and camera data. The RADAR data were processed using the CA-CFAR (Cell Averaging-Constant False Alarm Rate) algorithm, and the camera data were processed using YOLOv11 (You Only Look Once v11) to detect maritime obstacles. The EKF (Extended Kalman Filter) was then applied to the detection values from both sensors to track the maritime obstacles. These tracking results were then integrated using a track-to-track fusion method applying the WMA (Weighted Moving Average) algorithm. Finally, the proposed method was validated across a range of maritime encounter scenarios using a virtual simulator that emulates the maritime environment. As a result, the track-to-track fusion method recorded mean tracking errors of 9.91 m (95.81%) for position, 10.88° (71.10%) for COG (Course Over Ground), and 1.34 knots (79.68%) for SOG (Speed Over Ground), relative to the single-sensor-based tracking method (100%). Therefore, the track-to-track fusion method achieved reductions of 4.19% in position, 28.90% in COG, and 20.32% in SOG, significantly improving performance compared to the single-sensor-based tracking method. These results indicate that the track-to-track fusion method effectively enhances the reliability of maritime obstacle tracking. |
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| Publication Date | 2026-06-20 |
| Role | Corresponding Author |
| Category | SCIE |
| Impact Factor | 4.4 |
Ha-Yun Kim, Myung-Il Roh, Yeong-Min Jo, Hye-Won Lee, Yun-Sik Kim, In-Chang Yeo, "A Track-to-track Fusion Method for Tracking Maritime Obstacles", accepted for publication in International Journal of Naval Architecture and Ocean Engineering, 2026.06.20