基于CSI与SVM回归的室内定位方法  

An indoor positioning method based on CSI and SVM regression

作　　者：党小超 汝春瑞 郝占军 DANG Xiao-chao;RU Chun-rui;HAO Zhan-jun(College of Computer Science & Engineering,Northwest Normal University,Lanzhou 730070;Gansu Province Internet of Things Engineering Research Center,Lanzhou 730070,China)

机构地区：[1]西北师范大学计算机科学与工程学院,甘肃兰州730070 [2]甘肃省物联网工程研究中心,甘肃兰州730070

出　　处：《计算机工程与科学》2021年第5期853-861,共9页Computer Engineering & Science

基　　金：国家自然科学基金(61662070,61762079);甘肃省科技重点研发项目(1604FKCA097,17YF1GA015);甘肃省科技创新项目(17CX2JA037,17CX2JA039)。

摘　　要：为研究室内定位技术在复杂环境中的应用,以楼梯和实验室为实验场景,提出了一种基于信道状态信息(CSI)与SVM回归的室内定位方法。该方法通过基于密度的空间聚类方法(DBSCAN)去除信号噪声,并用主成分分析法(PCA)提取贡献最大的指纹特征,同时降低CSI指纹的维度。通过SVM回归建立CSI指纹与目标位置之间的非线性关系,从而达到根据测得的CSI指纹估计目标位置的目的。实验结果表明,在多径效应较强的楼梯复杂环境中,该定位系统可以在90%以上的概率下达到1 m的定位精度,实验室环境中可以在82%的概率下达到0.8 m的定位精度,这表明基于CSI与SVM回归的室内定位方法具有高效性和可行性。In order to study the application of indoor positioning technology in complex environments,using stairs and laboratories as experimental scenarios,an indoor positioning method based on channel state information(CSI)and SVM regression is proposed.The method removes signal noise by density-based spatial clustering(DBSCAN)and extracts the fingerprint features that contribute the most using principal component analysis(PCA),while reducing the CSI fingerprint dimension.The SVM regression is used to establish a non-linear relationship between the CSI fingerprint and the target position,so as to achieve the purpose of estimating the target position based on the measured CSI fingerprint.The experimental results show that the positioning system can achieve a positioning accuracy of 1 m with a probability of more than 90%in the complex environment of staircases with strong multipath effects,and a positioning accuracy of 0.8 m with a probability of 82%in a laboratory environment.It shows that the indoor positioning method based on CSI and SVM regression has high efficiency and feasibility.

关 键 词：室内定位技术 信道状态信息 DBSCAN算法 主成分分析法 SVM回归 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]