A Novel Data-Driven Scheme for the Ship Wake Identification on the 2-D Dynamic Sea Surface

Because the ship wake spreads into an area much larger than that of the ship hull in the sea surface, it has been widely used in the ship detection. However, due to the complex sea wave motion and the high sea state, the ship wake detection is still a challenging task. In this paper, we propose a novel data-driven method based on dynamic mode decomposition (DMD) to detect, reconstruct, and locate the Kelvin wake on the two-dimensional dynamic sea surface. Through the proposed method, the sea's dynamic characteristics including the oscillation frequency and decay/growth rate of ship wakes and the time-varying sea surface can be obtained. Meanwhile, the spatial features of ship wakes can be derived by dynamic modes as well. The proposed method can distinguish the dynamic characteristics between the Kelvin wake and sea background. Then the corresponding modes of the Kelvin wake can be successfully identified. The proposed method is demonstrated by analyzing a 2D sea surface where the Kelvin ship wake is superposed. It is found that our new approach provides an effective and accurate ship detection, even in the case of high sea states. Meanwhile, the extracted mode of the wake shows the ship position clearly with very high resolution.