文献类型：会议论文

英文题名：Nondestructive Inspection Techniques Based on Millimeter Wave Machine and A-priori Algorithm

作者：Cao, Xuejing[1]; Zhang, Weijia[1]; Wei, Tianfang[1]; Cheng, Xue[1]; Chen, Feiyu[1]; Xu, Haitao[1]; Li, Yulin[1]; Xie, Jin[1]; Li, Yibin[2]; Liu, Jing[2]

机构：[1] Shaoxing University, School of Mathematica Information, Shao Xing, China; [2] Zhejiang Heli Constructional Special Technology Co., Ltd, Ning Bo, China

会议论文集：2022 10th International Symposium on Next Generation Electronics, ISNE 2022

会议日期：May 5, 2023 - May 12, 2023

会议地点：Wuxi, China

语种：英文

外文关键词：Electromagnetic fields - Infrared devices - Inverse problems - Iterative methods - Millimeter waves - Nondestructive examination - Radar measurement - Signal detection

外文摘要：As a nondestructive and convenient technology, through-wall detection has played an essential role in many fields, such as construction, fire protection, and public security. This research proposes a new method for radar, which gives a hypothetical value of the data inverted by known information after obtaining the measurement data from radar and infrared detectors. It introduces an error matrix in the calculation process, which reduces the error of the data calculated by specific millimeter wave intensity detection method. The method proposed in this paper avoids the identification difficulties caused by errors and forms a joint inversion mechanism of microwave and near infrared with applicability and controllable error range, which is improved on the basis of an a-priori iterative operator. Considering that electromagnetic field inverse scattering has the problems of nonconvergence and significant error, this study aims to establish a subsurface analysis system with small result intervals through an emerging arithmetic iteration a-priori technique in the computer field. The method can also be applied to other fields of millimeter wave detection through inheriting, synthesizing, and innovating based on traditional algorithms in multisource information fusion, a-priori knowledge, inversion theory, and feature recognition. ? 2023 IEEE.