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首页> 外文期刊>Antennas and Propagation, IEEE Transactions on >GPU-Based Shooting and Bouncing Ray Method for Fast RCS Prediction
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GPU-Based Shooting and Bouncing Ray Method for Fast RCS Prediction

机译：基于GPU的射击和弹跳射线方法可实现快速RCS预测

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摘要

The shooting and bouncing ray (SBR) method is highly effective in the radar cross section (RCS) prediction. For electrically large and complex targets, computing scattered fields is still time-consuming in many applications like range profile and ISAR simulation. In this paper, we propose a GPU-based SBR that is fully implemented on the graphics processing unit (GPU). Based on the stackless kd-tree traversal algorithm, the ray tube tracing can rapidly evaluate the exit position in a single pass on the GPU. We also present a technique for fast electromagnetic computing that allows the geometric optics (GO) and Physical optics (PO) integral to be carried out on the GPU efficiently during the ray tube tracing. Numerical experiments demonstrate that the GPU-based SBR can significantly improve the computational efficiency of the RCS prediction, about 30 times faster, while providing the same accuracy as the CPU-based SBR.
机译：射击和弹跳射线（SBR）方法在雷达横截面（RCS）预测中非常有效。对于电大而复杂的目标，在许多应用中（例如距离分布图和ISAR仿真），计算散射场仍然很耗时。在本文中，我们提出了在图形处理单元（GPU）上完全实现的基于GPU的SBR。基于无堆栈kd树遍历算法，射线管跟踪可以在GPU上单次通过快速评估出口位置。我们还提出了一种用于快速电磁计算的技术，该技术允许在射线管跟踪期间在GPU上高效地执行几何光学（GO）和物理光学（PO）积分。数值实验表明，基于GPU的SBR可以显着提高RCS预测的计算效率，快30倍左右，同时提供与基于CPU的SBR相同的精度。

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