Two-dimensional short-range microwave holography algorithm and convolutional gridding

B. Lao, Jun-yi Wang, Jin-qing Wang, Jie-lin Fu, T. An, Hong-bing Qiu
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引用次数: 0

Abstract

A new-style two-dimensional short-range microwave holography imaging technique is introduced to compensate the inadequateness of short-range millimeter-wave holography imaging. In this method, the scattered data collected includes not only back-scattered but also forward-scattered data, and the incident field obtained by simulation or measurement, can further improve the imaging quality and resolution. A simulation in FEKO about the algorithm is given to provide reference for practical applications. In this paper, a new technique has been proposed to reduce the alias by using convolution function gridding the microwave holographic data which are irregularly data. To choose a proper convolution function, the performance of alias rejection of different convolution functions has been analyzed, validate the spheroidal function is the most rejection alias convolution function. To evaluate the proposed technique, the convolution gridding used to interpolate the irregularly holographic data onto a rectangular gird is examined in detail, validate the convolution gridding can be reduced aliasing. The convolution grid algorithm will be used in the actual imaging system to process the irregular data.
二维近程微波全息算法与卷积网格
介绍了一种新型的二维近距离微波全息成像技术,弥补了近距离毫米波全息成像的不足。该方法采集的散射数据既包括后向散射数据,也包括前向散射数据,通过模拟或测量得到的入射场可以进一步提高成像质量和分辨率。在FEKO中对该算法进行了仿真,为实际应用提供参考。本文提出了一种利用卷积函数对不规则的微波全息数据进行网格化处理的新方法。为了选择合适的卷积函数,对不同卷积函数的抗混叠性能进行了分析,验证了球面函数是最能抑制混叠的卷积函数。为了评价所提出的方法,对用于不规则全息数据插值到矩形网格上的卷积网格进行了详细的研究,验证了卷积网格可以减少混叠。在实际的成像系统中,将使用卷积网格算法对不规则数据进行处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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