Sampling Design of Synthetic Volume Arrays for Three-Dimensional Microwave Imaging
Sampling Design of Synthetic Volume Arrays for Three-Dimensional Microwave Imaging
复制标题
三维微波成像合成体阵列采样设计
DOI：
10.1109/tci.2018.2875332
复制
发表时间：
2018
影响因子：
5.4
通讯作者：
A. Yarovoy
中科院分区：
文献类型：
--
作者：
Jianping Wang;A. Yarovoy
本文讨论了三维合成阵列（即，合成体积阵列）用于微波成像。通常，一维或二维阵列的空间采样准则可以基于一些窄带/超宽带阵列理论来确定。然而，对于3-D阵列，其中天线位于体积中而不是在表面上，这些现有的阵列理论不再直接适用。为了解决空间采样问题的3-D阵列，我们制定了传感器/观察选择问题在本文中。虽然存在一些选择方法，并方便地适用于小规模的问题，他们要么效率较低，或提供较少的最佳结果的选择问题的数据尺寸为数百甚至数千，这是典型的微波成像。为了获得三维阵列的（近）最优空间采样方案，提出了一种基于最优性准则的贪婪算法--最小特征空间上的聚类最大投影（CMPME）算法。该算法试图通过考虑估计图像的误差阈值来选择最少的采样位置。此外，它具有更高的计算效率相比，现有的方法。最后通过成像实例验证了该方法的有效性和选择性能。
In this paper, sampling design of three-dimensional (3-D) synthetic array (i.e., synthetic volume array) for microwave imaging is considered. Generally, the spatial sampling criteria for one- or two-dimensional arrays can be determined based on some narrowband/ultrawideband array theories. However, for 3-D arrays, where antennas are located in a volume instead of over a surface, these existing array theories are no longer straightforwardly applicable. To address the spatial sampling problem of 3-D arrays, we formulate it as a sensor/observation selection problem in this paper. Although some selection approaches exist and are conveniently applicable to small-scale problems, they are either less efficient or provide less optimal results for selection problems with data dimensions of hundreds or even thousands which is typical for microwave imaging. To get the (near-) optimal spatial sampling scheme for 3-D arrays, a greedy algorithm named clustered maximal projection on minimal eigenspace (CMPME) is proposed to select the most informative sampling positions based on some optimality criteria. This algorithm attempts to select the fewest sampling positions by considering an error threshold for the estimated images. Moreover, it has higher computational efficiency compared to the existing approaches. Finally, its effectiveness and selection performances are demonstrated through some imaging examples.