Improving the Calibration by Power Balancing in Truncated Singular Value Decomposition for Wide-Band Scattering Parameter Data

IF 0.9 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Semih Doğu
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引用次数: 0

Abstract

The fi eld of microwave and millimeter-wave imaging systems is experiencing signi fi cant growth in research, primarily attributed to their versatile interaction capabilities with various materials. In the course of image reconstruction, the inverse scattering problem is resolved through either qualitative or quantitative means. The inverse scattering problem can be resolved by utilizing the truncated singular value decomposition (TSVD) algorithm, which is a suitable inversion method that can be employed when there is a need to discard a portion of data. The utilization of multi-frequency multi-static inversion techniques yields results that exhibit greater robustness and clarity when contrasted with single-frequency mono/bi-static con fi gurations. However, it should be noted that the TSVD formulation may not be su ffi cient in practical experimental situations, as it fails to account for potential deviations in power among excitations at varying frequencies. As a result, it is imperative to perform a calibration process to standardize the power variations across di ff erent frequencies. The proposal outlines a calibration methodology that aims to achieve power equilibrium across various frequencies and facilitate the automation of imaging algorithms. The proposed calibration technique is evaluated and demonstrated through the use of a through-the-wall experiment.
通过功率平衡改进宽波段散射参数数据的截断奇异值分解校准
微波和毫米波成像系统领域的研究正经历着显著的增长,这主要归功于它们与各种材料的多功能交互能力。在图像重建过程中,反向散射问题可通过定性或定量方法解决。利用截断奇异值分解(TSVD)算法可以解决反向散射问题,这是一种合适的反演方法,在需要舍弃部分数据时可以使用。与单频单静态/双静态反演技术相比,利用多频多静态反演技术得出的结果更稳健、更清晰。不过,需要注意的是,TSVD 公式在实际实验中可能并不适用,因为它未能考虑到不同频率激励之间功率的潜在偏差。因此,必须执行校准过程,使不同频率的功率变化标准化。该建议概述了一种校准方法,旨在实现不同频率下的功率平衡,并促进成像算法的自动化。通过穿墙实验对所提出的校准技术进行了评估和演示。
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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来源期刊
Electrica
Electrica Engineering-Electrical and Electronic Engineering
CiteScore
2.10
自引率
0.00%
发文量
59
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