A Material Identification Approach Based on Wi-Fi Signal

作     者：Chao Li Fan Li Wei Du Lihua Yin Bin Wang Chonghua Wang Tianjie Luo 

作者机构：Cyberspace Institute of Advanced TechnologyGuangzhou UniversityGuangzhou510700China PCL Research Center of Cyberspace SecurityPeng Cheng LaboratoryShenzhen518052China Department of Computer Science and Computer EngineeringUniversity of ArkansasFayetteville72701USA College of Electrical EngineeringZhejiang UniversityHangzhou310058China China Industrial Control Systems Cyber Emergency Response TeamBeijing100040China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第12期

页      面：3383-3397页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This work supports in part by National Key R&D Program of China(No.2018YFB2100400) National Science Foundation of China(No.61872100) Industrial Internet Innovation and Development Project of China(2019) PCL Future Regional Network Facilities for Large-scale Experiments and Applications(PCL2018KP001) Guangdong Higher Education Innovation Team(NO.2020KCXTD007) 

主　　题：Internet of Things Wi-Fi signal channel state information material identification noise elimination 

摘      要：Material identification is a technology that can help to identify the type of target *** approaches depend on expensive instruments,complicated pre-treatments and professional *** is difficult to find a substantial yet effective material identification method to meet the daily use *** this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level *** practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi *** that,the amplitude ratios and phase differences can be more stably utilized to classify the *** implement our system and evaluate its ability to identify materials in indoor *** result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.