Partition Detection and Location of a Kelvin Wake on a 2-D Rough Sea Surface by Feature Selective Validation

Abstract

The method for detecting and locating a Kelvin wake in the background of a two-dimensional (2-D), large-scale, random rough sea surface is studied. In this method, the large-scale, 2-D dynamic sea surface is partitioned, the correlation function between each partitioned sea surface is analyzed and extracted by the feature selective validation method, and the energy characteristic difference measures between each partitioned sea surfaces are obtained. By averaging the difference measures between the target partitioned sea surface and the adjacent sea surface, we can obtain the average value of the characteristic difference measure of each partitioned sea surface. Through the simulation and calculation, it was determined that, after the Kelvin wakes are superimposed on the partitioned sea surface, the characteristic difference measures are clearly larger than that of the partitioned sea surface without superimposed wakes. This finding shows that the method can effectively detect the changes in the sea surface wave energy caused by the wakes. The result shows that the method can accurately and efficiently identify the location of the partitioned sea surface where the Kelvin wakes are superimposed, after selecting a reasonable threshold, to realize the accurate location of the Kelvin wakes.