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

作     者：Xu Yubin Sun Yongliang Ma Lin 

作者机构：Communication Research Center Harbin Institute of Technology Harbin 150080 P. R. China 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2011年第17卷第3期

页      面：223-229页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 081601[工学-大地测量学与测量工程] 0816[工学-测绘科学与技术] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

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.