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13 October 2017 A parallel efficient partitioning algorithm for the statistical model of dynamic sea clutter at low grazing angle
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Study of characteristics of sea clutter is very important for signal processing of radar, detection of targets on sea surface and remote sensing. The sea state is complex at Low grazing angle (LGA), and it is difficult with its large irradiation area and a great deal simulation facets. A practical and efficient model to obtain radar clutter of dynamic sea in different sea condition is proposed, basing on the physical mechanism of interaction between electromagnetic wave and sea wave. The classical analysis method for sea clutter is basing on amplitude and spectrum distribution, taking the clutter as random processing model, which is equivocal in its physical mechanism. To achieve electromagnetic field from sea surface, a modified phase from facets is considered, and the backscattering coefficient is calculated by Wu’s improved two-scale model, which can solve the statistical sea backscattering problem less than 5 degree, considering the effects of the surface slopes joint probability density, the shadowing function, the skewness of sea waves and the curvature of the surface on the backscattering from the ocean surface. We make the assumption that the scattering contribution of each facet is independent, the total field is the superposition of each facet in the receiving direction. Such data characters are very suitable to compute on GPU threads. So we can make the best of GPU resource. We have achieved a speedup of 155-fold for S band and 162-fold for Ku/Χ band on the Tesla K80 GPU as compared with Intel® Core™ CPU. In this paper, we mainly study the high resolution data, and the time resolution is millisecond, so we may have 10,00 time points, and we analyze amplitude probability density distribution of radar clutter.
Conference Presentation
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Tao Wu, Zhensen Wu, and Longxiang Linghu "A parallel efficient partitioning algorithm for the statistical model of dynamic sea clutter at low grazing angle", Proc. SPIE 10422, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2017, 104220N (13 October 2017);


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