2D mm-wave imaging based on singular value decomposition

B. Mamandipoor, Upamanyu Madhow, A. Arbabian
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引用次数: 1

Abstract

In this paper, we develop a systematic framework for two-dimensional mm-wave imaging, based on the singular value decomposition (SVD) of the Helmholtz wave equation under the Born approximation. We identify the degrees of freedom as a function of the geometry of the aperture and the scene, and provide insight into the eigenmodes identified by the SVD. For sparse arrays with number of elements smaller than the degrees of freedom, we propose, and experimentally demonstrate the efficacy of, an eigen-filtered pseudo-inverse algorithm which selects the eigenmodes being imaged.
基于奇异值分解的二维毫米波成像
本文基于波恩近似下亥姆霍兹波动方程的奇异值分解(SVD),建立了二维毫米波成像的系统框架。我们将自由度确定为光圈几何形状和场景的函数,并提供由SVD识别的特征模式的见解。对于元素数小于自由度的稀疏阵列,我们提出并实验证明了一种特征滤波伪逆算法的有效性,该算法选择被成像的特征模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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