The quantitative inspection of unknown targets or bodies by means of microwave tomography requires a proper modeling of the field scattered by the structures under test, which in turn depends on several factors related to the adopted antennas and measurement configuration. In this article, a multifrequency tomographic approach in nonconstant-exponent Lebesgue spaces is enhanced by a preliminary step that processes the measured scattered field with a neural network based on long short-term memory cells. In the considered cases, this approach allows dealing with measurements in three-dimensional settings obtained with non-ideal antennas and measurement points, while retaining a canonical two-dimensional formulation of the inverse problem. The adopted data-driven model is trained with a set of simulations of cylindrical targets performed with a finite-difference time domain method, considering a simplified bistatic measurement configuration as an initial case study. The inversion procedure is then validated with numerical simulations involving cylindrical and spherical structures.

中文翻译：

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基于LSTM的散射场处理的微波层析成像

通过微波层析成像技术对未知目标或物体进行定量检查，需要对被测结构散射的场进行适当的建模，而这又取决于与采用的天线和测量配置有关的几个因素。在本文中，通过一个初步步骤来增强非恒定指数Lebesgue空间中的多频层析成像方法，该步骤可以使用基于长短期记忆细胞的神经网络处理所测量的散射场。在考虑的情况下，此方法允许处理使用非理想天线和测量点获得的三维设置中的测量，同时保留反问题的规范二维表示。采用的数据驱动模型通过一组圆柱目标的模拟进行训练，该模拟以有限差分时域方法执行，并将简化的双基地测量配置作为初始案例研究。然后用涉及圆柱和球形结构的数值模拟对反演过程进行验证。