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基于WiFi的分级室内定位
引用本文:王行娟.基于WiFi的分级室内定位[J].电讯技术,2021,61(10):1291-1296.
作者姓名:王行娟
作者单位:武汉华夏理工学院 信息工程学院,武汉430223
基金项目:湖北省科研计划项目(B2020314,B2020309)
摘    要:在经典的K最近邻(K-Nearest Neighbors,KNN)的WiFi定位方法中,其算法复杂度随着定位区域和定位区域内的WiFi接入点(Access Point,AP)的增加而增加,无法满足实时定位的要求.为此,提出一种分级WiFi定位算法.算法分为粗定位和精定位阶段,首先通过AP的可见性利用汉明距离寻找可能的子区域,再用KNN算法在子区域内(利用信号强度欧氏距离)进行精定位.经过实测数据验证,平均单次定位时间在KNN算法下下降了约95%,在最大后验算法下下降了约96%,表明所提分级定位框架具有延迟低的优点.

关 键 词:室内定位  WiFi定位  K最近邻  分级结构

WiFi-based indoor positioning by adopting a hierarchical structure
WANG Xingjuan.WiFi-based indoor positioning by adopting a hierarchical structure[J].Telecommunication Engineering,2021,61(10):1291-1296.
Authors:WANG Xingjuan
Affiliation:School of Information Engineering,Wuhan Huaxia University of Technology,Wuhan 430223,China
Abstract:WiFi based indoor positioning has great potential applications in location based service(LBS),due to its independence of extra hardware.However,in the classical K nearest neighbors(KNN) algorithm adopted in WiFi based positioning,the computational cost increases with the size of the area and the number of access points(APs),and it cannot fulfill the requirement of realtime implementation.In this paper,a hierarchical positioning method is proposed for WiFi based positioning.The method includes two phases:coarse positioning and accurate positioning.In coarse positioning phase,some possible sub regions are found according to the Hamming distance.In accurate positioning phase,the classical KNN algorithm is adopted.The proposed method can decrease the computation cost and accelerate the positioning.Experiment shows that the proposed method has a decrease of about 95% and 96% in average positioning time under the KNN method and the maximum posterior probability method,respectively.
Keywords:indoor positioning  WiFi based positioning  K nearest neighbors  hierarchical structure
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