FAST RCS PREDICTION USING MULTIRESOLUTION SHOOTING AND BOUNCING RAY METHOD ON THE GPU

Peng Cheng Gao

doi:10.2528/PIER10061807

FAST RCS PREDICTION USING MULTIRESOLUTION SHOOTING AND BOUNCING RAY METHOD ON THE GPU

2010-01-01

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DOI：10.2528/PIER10061807

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This paper presents a GPU-based multiresolution shooting and bouncing ray (MSBR) method with the kd-tree acceleration structure for the fast radar cross section (RCS) prediction of electrically large and complex targets. The multiresolution grid algorithm can greatly reduce the total number of ray tubes, as it adaptively adjusts the density of ray tubes for regions with different complexities of their structures, while the kd-tree acceleration structure can highly decrease the number of ray-patch intersection tests. The multiresolution grid technique and kd-tree traversal algorithm are fully implemented on the GPU to further accelerate the SBR by exploiting the massively parallel computing ability. Numerical experiments demonstrate that the proposed GPU-based MSBR can significantly improve the computational efficiency. It is about 40 times faster than the CPU MSBR, and at least 4.8 times faster than the GPU-based SBR without the multiresolution grid algorithm.

Keyword:

HIGH-FREQUENCY RCS

DIVISION

ALGORITHM

TARGETS

ACCELERATION

SCATTERING

REDUCTION

SBR

FAST RCS PREDICTION USING MULTIRESOLUTION SHOOTING AND BOUNCING RAY METHOD ON THE GPU

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