A singular-value method for reconstruction of nonradial and lossy objects.
A singular-value method for reconstruction of nonradial and lossy objects.
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用于重建非径向和有损物体的奇异值方法。
DOI：
10.1109/tuffc.2012.2233
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Waag,Robert
中科院分区：
文献类型：
--
作者：
Jiang,Wei;Astheimer,Jeffrey;Waag,Robert
使用奇异值分解形成散射算子的降阶表示，提出了针对非径向有损物体的高效逆散射算法。这些算法扩展了不适用于非径向有损散射物体的特征函数方法，因为这些物体的散射算子没有正交特征函数分解。还提出了一种通过分离来自不同局部区域的散射贡献来进行局部重建的方法。通过形成散射算子的降阶表示来隔离每个区域的散射，该算子的域和范围空间由远场模式组成，重传场集中于局部区域。还给出了散射物体边界、平均声速和平均衰减斜率的估计方法。这些方法产生的散射物体的近似值足够精确，可以在单次迭代中重建残余变化。使用具有随机背景、内部特征和白噪声的有损椭圆形物体计算的散射来评估所提出的方法。局部重建产生的图像的空间分辨率比中心频率的半波长还要精细，并以相对均方根误差分别为 1.09% 和 11.45% 的方式再现声速和衰减斜率。
Efficient inverse scattering algorithms for nonradial lossy objects are presented using singular-value decomposition to form reduced-rank representations of the scattering operator. These algorithms extend eigenfunction methods that are not applicable to nonradial lossy scattering objects because the scattering operators for these objects do not have orthonormal eigenfunction decompositions. A method of local reconstruction by segregation of scattering contributions from different local regions is also presented. Scattering from each region is isolated by forming a reduced-rank representation of the scattering operator that has domain and range spaces comprised of far-field patterns with retransmitted fields that focus on the local region. Methods for the estimation of the boundary, average sound speed, and average attenuation slope of the scattering object are also given. These methods yielded approximations of scattering objects that were sufficiently accurate to allow residual variations to be reconstructed in a single iteration. Calculated scattering from a lossy elliptical object with a random background, internal features, and white noise is used to evaluate the proposed methods. Local reconstruction yielded images with spatial resolution that is finer than a half wavelength of the center frequency and reproduces sound speed and attenuation slope with relative root-meansquare errors of 1.09% and 11.45%, respectively.