首页> 外国专利> POSSIBILISTIC FUZZY C-MEANS GROUPING BASED MIXED WIRELESS INDOOR POSITIONING METHOD WHICH USES K-NEAREST NEIGHBOR AND GUSTAFSON-KESSEL METHODS CAPABLE OF IMPROVING DATABASE SEARCH EFFICIENCY

POSSIBILISTIC FUZZY C-MEANS GROUPING BASED MIXED WIRELESS INDOOR POSITIONING METHOD WHICH USES K-NEAREST NEIGHBOR AND GUSTAFSON-KESSEL METHODS CAPABLE OF IMPROVING DATABASE SEARCH EFFICIENCY

机译:基于K-NEAREST NEIGHBOR和GUSTAFSON-KESSEL方法的可能的模糊C均值分组混合无线室内定位方法,可提高数据库的搜索效率

摘要

PURPOSE: A PFCM(Possibilistic Fuzzy C-Means) grouping based mixed wireless indoor positioning method which uses KNN(K-Nearest Neighbor) and GK(Gustafson-Kessel) methods is provided to measure a signal noise ratio received from a plurality of access points in a training step, thereby building a wireless fingerprint type database.;CONSTITUTION: A database is built with first propagation characteristic value data in a wireless fingerprint mode(S110). A standard point is searched using a PFCM(Possibilistic Fuzzy C-Means) mixed technique. k standard points are selected using a KNN(K-Nearest Neighbor) technique(S120). A group of the first propagation characteristic value data of the k standard points is formed(S130). A group which has a minimum GK(Gustafson-Kessel) distance with respect to the first propagation characteristic value data and a group center vector are selected(S140). The average of standard point position coordinates belonged to the selected group is calculated. The position of a terminal is estimated(S150).;COPYRIGHT KIPO 2012
机译:目的:提供一种基于PFCM(可能性模糊C均值)分组的混合无线室内定位方法,该方法使用KNN(最近邻)和GK(Gustafson-Kessel)方法来测量从多个接入点接收的信号噪声比在训练步骤中,从而建立无线指纹类型数据库。组成:使用无线指纹模式的第一传播特征值数据建立数据库(S110)。使用PFCM(可能性模糊C均值)混合技术搜索标准点。使用KNN(K最近邻)技术选择k个标准点(S120)。形成一组k个标准点的第一传播特性值数据(S130)。选择相对于第一传播特性值数据具有最小GK(Gustafson-Kessel)距离的组和组中心向量(S140)。计算属于所选组的标准点位置坐标的平均值。估计终端的位置(S150)。; COPYRIGHT KIPO 2012

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