Abstract
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.
Highlights
Ship wake is a vital ship signature in marine surveillance because it contains sufficient ship information [1], [2]
We propose a new data-driven scheme based on the dynamic mode decomposition (DMD) for the Kelvin wake detection
SIMULATION OF SEA SURFACE WITH SHIP WAKES we mainly introduce the modeling of the 2-D dynamic sea surface with the Kelvin wake
Summary
Ship wake is a vital ship signature in marine surveillance because it contains sufficient ship information [1], [2]. In the absence of sufficient data to support the training process, it is still challenging to identify or locate the ship wake in the sea surface through these methods. To address this challenge, we propose a new data-driven scheme based on the dynamic mode decomposition (DMD) for the Kelvin wake detection. The extracted spatial-temporal subspace could conveniently capture the main dynamic characteristics of the time-varying sea surface with the wake This could provide a powerful tool for analyzing large-scale marine monitoring.
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