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The surface reflected wave is formed by the wave of the sea surface when the submarine is sailing underwater. The intensity of the reflected wave decreases with the increase of the depth of the submarine, which makes it difficult to detect the submarine based on the height characteristics of the surface wave. For this problem, the theory of underwater submarine detection is studied based on the relationship between the intensity image of lidar and the normal vector distribution of target surface. First, the two-scale wave theory was proposed to establish a simulation model of the sea surface wave, and the microsurface element normal vector was solved for the gridded sea surface. Under the premise of considering the shielding effect, the intensity image of the sea surface wave including the submarine reflected wave was obtained by coupling the lidar equation with the sea surface wave model. Finally, principal component analysis (PCA) and BP neural network are used to extract and recognize the characteristics of submarine and surface intensity images. The results show that at low wind speed and small wind field, the recognition rate of the submarine with a depth of 19m is more than 90%. With the increase of the depth, wind speed and wind field, the recognition rate decreases gradually. This study provides a new idea for lidar submarine reflection wave detection.
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