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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >MIMO Ground Penetrating Radar Imaging Through Multilayered Subsurface Using Total Variation Minimization
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MIMO Ground Penetrating Radar Imaging Through Multilayered Subsurface Using Total Variation Minimization

机译：使用总变化最小化的多层地下MIMO地面穿透雷达成像

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摘要

In most of the existing ground penetrating radar (GPR) imaging algorithms, using either full or sparse data collection, the ground is modeled as a single half-space layer. In this paper, a generalized sparse imaging approach with total variation minimization (TVM) for multiple-input multiple-output (MIMO) GPR imaging through multilayered subsurface is proposed. The multilayered media Green's function is incorporated in the imaging algorithm to take into account the complex wave propagation effects under multilayered subsurface. An analytical expression of the layered subsurface Green's function is derived using the stationary-phase method, which significantly reduces the computation time and complexity. On the other hand, as TVM minimizes the gradient of the image, its incorporation in the imaging algorithm results in a reconstruction that preserves the geometry and edges of the targets better than the standard Li-minimization-based sparsity-driven imaging. The number of antenna elements and frequency measurements in MIMO GPR system can he significantly reduced using the proposed technique without degradation of the image quality. Although MIMO configuration is investigated in this paper, the presented approach can be simply applied to monostatic synthetic aperture radar.
机译：在大多数现有的探地雷达（GPR）成像算法中，使用完整或稀疏数据收集，都将地面建模为单个半空间层。本文提出了一种通过多层地下多输入多输出（MIMO）GPR成像的具有总变化最小化（TVM）的广义稀疏成像方法。多层介质的格林函数被纳入成像算法中，以考虑到多层地下下的复杂波传播效应。使用固定相方法导出了分层的地下格林函数的解析表达式，这大大减少了计算时间和复杂性。另一方面，由于TVM最小化了图像的梯度，因此将其合并到成像算法中可以比传统的基于Li最小化的稀疏驱动成像更好地保留目标的几何形状和边缘。使用所提出的技术，可以显着减少MIMO GPR系统中的天线元件数量和频率测量，而不会降低图像质量。尽管本文研究了MIMO配置，但所提出的方法可以简单地应用于单静态合成孔径雷达。

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