This study addresses the classification of objects using their electromagnetic signatures with convolutional neural networks (CNNs) trained on noiseless data. The singularity expansion method (SEM) was applied to establish a compact model that accurately represents the ultra-wideband scattered field (SF) of an object, independently of its orientation and observation angle. To perform the classification, we used a CNN associated with a noise-robust SEM technique to classify different objects based on their characteristic parameters. To validate this approach, we compared the performance of the classifier with and without SEM pre-processing of the SF for different noise levels and for object sizes not present in the training set. Moreover, we propose a procedure that determines the direction of the receiving antenna and orientation of an object based on the residues associated with each complex natural resonance. This classification procedure using pre-processed SEM data is promising and easy to train, especially when generalizing to object sizes not included in the training set.

中文翻译：

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使用卷积神经网络根据物体的电磁特征对物体的形状和方向进行分类

这项研究通过利用无噪声数据训练的卷积神经网络 (CNN) 的电磁特征来解决物体的分类问题。应用奇点展开法（SEM）建立了一个紧凑的模型，该模型能够准确地表示物体的超宽带散射场（SF），而与物体的方向和观察角度无关。为了进行分类，我们使用了 CNN 和抗噪声 SEM 技术，根据不同对象的特征参数对它们进行分类。为了验证这种方法，我们针对不同的噪声水平和训练集中不存在的对象大小，比较了有和没有 SF SEM 预处理的分类器的性能。此外，我们提出了一种程序，根据与每个复杂的自然谐振相关的残差来确定接收天线的方向和物体的方向。这种使用预处理 SEM 数据的分类过程很有前途且易于训练，特别是在推广到训练集中未包含的对象尺寸时。