Machine Learning-Based Monostatic Microwave Radar for Building Material Classification

被引：0

作者：

Alsaleh, Nawal ^{[1
,2
]}

Pomorski, Denis ^{[1
]}

Sebbache, Mohamed ^{[2
,3
,4
]}

Haddadi, Kamel ^{[2
,3
,4
]}

机构：

[1] Univ Lille, CNRS, UMR 9189, CRIStAL, F-59000 Lille, France

[2] Univ Polytehc Hauts de France, CNRS, Univ Lille, Cent Lille,UMR 8520,IEMN, Lille, France

[3] IEMN, UMR 8520, Lille, France

[4] Univ Lille, CNRS 3380, IRCICA, Lille, France

来源：

2023 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE, I2MTC | 2023年

关键词：

Microwave nondestructive testing and evaluation (MNDT&E); microwave radar; material characterization; machine learning (ML); classification; MODEL;

D O I：

10.1109/I2MTC53148.2023.10176071

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A Microwave nondestructive testing and evaluation (MNDT&E) method combining a broadband continuous wave (CW) monostatic radar and machine learning (ML) algorithms is proposed for building materials classification. A low-power and compact system using commercial solutions is considered. The data collected, i.e. complex reflection coefficient S11, are pre-processed then used as inputs to train support vector machine (SVM) and decision tree classifier (DCT) algorithms. The trained ML models achieved accuracy of 97.88% for DCT and 88.60% for SVM confirming the interest of the method for contactless building material classification.

引用

页数：5

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