%0 Journal Article
%T WiFi信号传播与无线AP定位研究<br>Study of WiFi Signal Propagation and the Location of the Wireless AP
%A 陈丽琴
%A 高凌宇
%A 杨晶晶
%A 黄铭
%J Hans Journal of Wireless Communications
%P 26-35
%@ 2163-3991
%D 2021
%I Hans Publishing
%R 10.12677/HJWC.2021.112004
%X 由于泛在、传输速率高和免费等特点，WiFi是最受欢迎的上网方式，无线AP也是目前世界上数量最多的在用无线电收发设备。因此，研究WiFi信号传播与无线AP定位是掌握无线电监测技术基础的理想实现平台。本文基于电磁场仿真软件WinProp和人工智能AI算法，结合一栋单层别墅的无线电传播特性研究了无线AP定位技术。仿真结果表明：采用卷积神经网络CNN、K邻近算法KNN和支持向量机SVM，监测接收点的数目为3时，识别无线AP所在房间的准确率分别为76.7%、76.3%和.70.0%；数目为5时，识别准确率分别为83.6%、91.4%和87.9%；数目为7时，准确率分别为92.6%、98.1%和96.8%。上述工作对理解非视通环境下的无线电监测定位原理有重要意义。<br />
Due to its ubiquity, high transmission rate, and free of charge, WiFi is the most popular way to access the Internet, and wireless AP is currently the world’s largest number of radio transceiver devices in use. Therefore, studying WiFi signal propagation and wireless AP positioning is an ideal platform for mastering the basics of radio monitoring technology. Based on the electromagnetic field simulation software WinProp and artificial intelligence algorithm, combined with the radio propagation characteristics of a one-story villa, the wireless AP positioning technology is studied in this paper. The simulation results show that: using convolutional neural network, K neighboring algorithm and support vector machine, when the number of monitoring receiving points is 3, the accuracy rates of identifying wireless AP are 76.7%, 76.3% and 70.0% respectively; when the number is 5, the recognition accuracy rates are 83.6%, 91.4%, and 87.9%, respectively; when the number is 7, the accuracy rates are 92.6%, 98.1%, and 96.8%, respectively. The above work is of great significance for understanding the principle of radio monitoring and positioning in a Non Line of Sight environment.
%K WiFi，非视通定位，无线电监测<br>WiFi
%K Non Line of Sight Location
%K Radio Monitoring
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=41994