基于信道状态信息的无源室内定位

摘要

针对传统室内定位方法在准确性及稳定性上的不足,本文提出了一种基于信道状态信息(channel state information,CSI)的无源室内定位方法.该方法采用普通设备搭建了实验平台,离线阶段采集CSI数据建立位置指纹库,在线阶段则利用机器学习的朴素贝叶斯算法进行位置分类.为进一步提高分类准确度,本文还提出了置信度方法,通过综合多条天线对的结果来减少位置误判.实验结果表明,本文所提出方法能有效实现对室内人员的无源定位,可以达到90%以上的准确度.%To deal with the shortcomings of indoor localization in terms of accuracy and stability, a method based on channel state information (CSI) was proposed to realize passive indoor positioning.A platform was set up with off-the-shelf equipment to collect CSI data.During the offline stage of the method, gathered data from each location were stored as a fingerprint in the database.During the online stage, naive Bayes classification from machine learning was utilized to classify the locations.Furthermore, the degree of confidence was proposed to combine the estimation from different antenna pairs.Result shows that the proposed method can effectively realize passive indoor human localization with an accuracy of more than 90%.