Millimeter-wave (MMW) imaging is becoming an important option in many sensing applications. However, the resulting images are often plagued with artifacts caused by complex target scenarios such as concave structures, hampering applications where precise recognition is emphasized. It has been shown that existing imaging techniques can effectively resolve this issue by considering the multi-reflection propagation process in the forward model of the inverse problem. But the accuracy of such method still depends on the precise separation of reflected signals exhibiting different number of interactions with the target surfaces. In this article, an improved imaging technique based on circular polarizations is proposed for accurate imaging of concave objects. By utilizing circular polarized measurements, the received signal can be divided into odd and even number of reflection times. Then, an iterative reconstruction technique is introduced to automatically separate signal components and reconstruct precise contours of the concave surfaces. Furthermore, a strict observation angle boundary model is derived based on methods of the stationary phase to correct the image deformation of edges existing in previous algorithms. Both numerical and experimental results synthesized from 6∼18 GHz dual-polarized measurements are used to demonstrate the improved accuracy and automation of the proposed method.

中文翻译：

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使用圆偏振对凹面物体进行精确的近场毫米波成像

毫米波 (MMW) 成像正在成为许多传感应用中的重要选择。然而，由此产生的图像经常受到由复杂目标场景（例如凹结构）引起的伪影的困扰，阻碍了强调精确识别的应用。已经表明，现有的成像技术可以通过在逆问题的前向模型中考虑多反射传播过程来有效地解决这个问题。但是这种方法的准确性仍然取决于反射信号的精确分离，这些反射信号表现出与目标表面不同数量的相互作用。在本文中，提出了一种基于圆偏振的改进成像技术，用于对凹面物体进行精确成像。通过利用圆偏振测量，接收信号可分为奇数次和偶数次反射次数。然后，引入迭代重建技术来自动分离信号分量并重建凹面的精确轮廓。此外，基于平稳相的方法推导出严格的观察角边界模型，以校正现有算法中存在的图像边缘变形。从 6∼18 GHz 双极化测量合成的数值和实验结果都用于证明所提出方法的准确性和自动化程度的提高。基于平稳相的方法推导出严格的观察角边界模型，以校正现有算法中存在的图像边缘变形。从 6∼18 GHz 双极化测量合成的数值和实验结果都用于证明所提出方法的准确性和自动化程度的提高。基于平稳相的方法推导出严格的观察角边界模型，以校正现有算法中存在的图像边缘变形。从 6∼18 GHz 双极化测量合成的数值和实验结果都用于证明所提出方法的准确性和自动化程度的提高。