M.Sc. Thesis
2026.03.19 14:34
Yun-Sik Kim, "An Improved Method for Detection and Tracking of Maritime Obstacles Based on Multiple Sensor Fusion", M.Sc. Thesis, Seoul National University, 2026.02.25
조회 수 54
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| Abstract | For the safe and reliable navigation of USVs (Unmanned Surface Vehicles), it is essential that they autonomously recognize maritime obstacles and accurately detect their surroundings. To achieve this, USVs are typically equipped with multiple sensors, such as RADAR (RAdio Detection And Ranging) and a camera, each possessing distinct advantages and limitations. RADAR provides long-range detection and relative speed measurement, but its low resolution limits object classification and lateral distance accuracy. In contrast, the camera provides high resolution visual information for detailed recognition, but it is sensitive to lighting conditions. Because of differences in sensing range, frequency, and error characteristics, achieving consistent, accurate obstacle tracking with a single sensor is challenging. Therefore, it is necessary to integrate multiple sensors, combining RADAR's robustness across various weather conditions with a camera's high resolution capabilities. To address these challenges, this study proposes an improved method for detecting and tracking maritime obstacles based on multiple sensor fusion. The proposed method improves overall reliability in two steps. First, the tracking performance of each sensor—RADAR and the camera—is enhanced to ensure that both sensors can independently deliver accurate, stable tracking results. Then, the two sensors are combined through sensor fusion to complement each other, enabling the system to achieve higher accuracy and stability in tracking maritime obstacles. The method consists of three main stages: (1) detection and localization, (2) tracking, and (3) sensor fusion. In the detection and localization stage, the camera uses the YOLO (You Only Look Once) object detection model and a depth-based localization method to mitigate the instability of the conventional horizon-based localization method. For RADAR, obstacle detection and localization are performed using the CA-CFAR (Cell-Averaging Constant False Alarm Rate) algorithm. This representative RADAR signal-processing method ensures robust detection even in cluttered maritime environments. In the tracking stage, a hybrid tracking method is developed by combining the EKF (Extended Kalman Filter)-based models with a learning-based model. This hybrid method leverages the interpretability and stability of EKF-based tracking and the adaptability of learning-based tracking, providing robust performance across varying maritime environments. Finally, in the sensor fusion stage, the tracking results from RADAR and the camera are integrated using a sensor-level fusion method based on the estimated error covariance, yielding accurate and stable tracking performance. This study validated the proposed method through field tests using RADAR and the camera. The results demonstrate that the proposed method significantly improves detection accuracy and tracking stability compared to single-sensor methods. These results confirm that the proposed method effectively enhances situational awareness and navigation safety for USVs operating in complex maritime environments. |
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| Publication Date | 2026-02-25 |
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Yun-Sik Kim, "An Improved Method for Detection and Tracking of Maritime Obstacles Based on Multiple Sensor Fusion", M.Sc. Thesis, Seoul National University, 2026.02.25
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