A KNN-based two-step fuzzy clustering weighted algorithm for WLAN indoor positioning  

作　　者：Xu Yubin Sun Yongliang Ma Lin 

机构地区：[1]Communication Research Center, Harbin Institute of Technology, Harbin 150080, P. R. China

出　　处：《High Technology Letters》2011年第3期223-229,共7页高技术通讯（英文版）

摘　　要：Although k-nearest neighbors （KNN） is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means （FCM） clustering algorithm is applied to improve it. Thus, a KNN-based two-step FCM weighted （KTFW） algorithm for indoor positioning in wireless local area networks （WLAN） is presented in this paper. In KTFW algorithm, k reference points （RPs） chosen by KNN are clustered through FCM based on received signal strength （RSS） and location coordinates. The right clusters are chosen according to rules, so three sets of RPs are formed including the set of k RPs chosen by KNN and are given different weights. RPs supposed to have better contribution to positioning accuracy are given larger weights to improve the positioning accuracy. Simulation results indicate that KTFW generally outperforms KNN and its complexity is greatly reduced through providing initial clustering centers for FCM.

关 键 词：wireless local area networks （WLAN） indoor positioning k-nearest neighbors （KNN） fuzzy c-means （FCM） clustering center 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] P228[自动化与计算机技术—计算机科学与技术]