A new bistatic model for electromagnetic scattering from randomly rough surfaces

A new bistatic model for electromagnetic scattering from randomly rough surfaces
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随机粗糙表面电磁散射的新双基地模型

DOI:
10.1080/17455030701459902
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发表时间:
2008-01
影响因子:
--
通讯作者:
--
中科院分区:
物理与天体物理3区
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--
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在本文中,我们提出了一种用于小到中等高度的高斯粗糙表面电磁散射的双基地模型。它基于积分方程公式,其中格林函数及其梯度的光谱表示具有完整的形式,这种通用方法类似于高级积分方程模型 (AIEM) 和二阶多重散射积分方程模型 (IEM2M) 中使用的方法。然而,这个新模型可以被视为这两个模型的扩展,有两个原因:首先,它在评估单次散射的互补散射系数时做出了越来越少的限制性假设,其次,它通过包含交叉和互补散射系数的误差函数相关项来包含更严格的分析,这些项源于格林函数光谱表示中的绝对相位项。即使忽略误差函数相关项的影响,预计我们的互补散射系数结果也会更准确且更通用。因此,所提出的模型有望具有更广泛的适用性和更高的准确性。提供数值模拟来证明所提出模型的有效性。
In this paper we propose a bistatic model for electromagnetic scattering from a Gaussian rough surface with small to moderate heights. It is based on the integral equation formulation where the spectral representations of the Green's function and its gradient are in complete forms, a general approach similar to those used in the advanced integral equation model (AIEM) and the integral equation model for second-order multiple scattering (IEM2M). Yet this new model can be regarded as an extension to these two models on two accounts: first it has made fewer and less restrictive assumptions in evaluating the complementary scattering coefficient for single scattering, and second it contains a more rigorous analysis by the inclusion of the error function related terms for the cross- and complementary scattering coefficients, which stems from the absolute phase term in the spectral representation of the Green's function. It is expected that our result for the complementary scattering coefficient is more accurate and more general, even when the effect of the error function related terms is neglected. As a result, the proposed model is expected to have wider applicability with a better accuracy. Numerical simulations are provided to demonstrate the validity of the proposed model.
DOI: 10.1109/igarss.1996.516906
发表时间: 1996-05
期刊: IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium
影响因子: --
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影响因子: 8.2
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发表时间: 2001-07
期刊: Waves in Random Media
影响因子: --
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