[1]王田芳[],张玲华[].基于模糊分类和AP加权的室内定位算法[J].计算机技术与发展,2017,27(04):46-50.
　WANG Tian-fang[],ZHANG Ling-hua[]. Indoor Localization Algorithm with Fuzzy Classification and AP Weighting[J].,2017,27(04):46-50.
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基于模糊分类和AP加权的室内定位算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年04期
页码:
46-50
栏目:
智能、算法、系统工程
出版日期:
2017-04-10

文章信息/Info

Title:
 Indoor Localization Algorithm with Fuzzy Classification and AP Weighting
文章编号:
1673-629X（2017）04-0046-05
作者:
 王田芳[1]; 张玲华[2]
1. 南京邮电大学 通信与信息工程学院；2.江苏省通信与网络技术工程研究中心
Author(s):
 WANG Tian-fang[1]; ZHANG Ling-hua[2]
关键词:
 ; 室内定位; 指纹定位法;  模糊分类;  AP加权; 稀疏度
Keywords:
 ; indoor localization; fingerprint localization; fuzzy classification; AP weighting; sparse degree
分类号:
TP393
文献标志码:
A
摘要:
 为了提高指纹定位法的在线定位效率,提出了最强AP模糊分类(SAF)算法.该算法以模糊值为信号强度波动范围并将参考点划分到所有可能的AP类中,降低了参考点类别误判的风险.在此基础上提出了分方向AP加权(DW)算法来改善传统定位算法的定位精度.该算法分析了在不同分类区域中各AP分别对 X 和 Y 方向上定位结果的影响程度,以此为依据为各个AP赋予分方向加权系数,并将加权系数融合到匹配算法中.此外,针对参考点较为稀疏的情况,提出了稀疏度的概念并给出了参考点稀疏度较大时的处理方法.仿真结果表明,与传统定位算法相比,提出的算法在线阶段遍历的参考点数目大约减少了70%,同时平均定位误差降低了15%左右,有效提高了定位精度并增强了定位实时性.
Abstract:
 In order to improve the online positioning efficiency of the fingerprint localization,a fuzzy classification algorithm based on the Strongest AP (SAF) has been proposed.The risk of misjudging the class of reference points has been reduced through determining signal fluctuation range with fuzzy value and dividing the reference points into all possible classes.On this basis,directional AP weighting (DW) algorithm has been presented to obtain higher accuracy.Based on the impact of X and Y direction in different classification area,this algorithm weights the APs different values and integrates them into the matching algorithm in different classification area.In addition,in view of the sparse of reference points,the concept of sparse degree has been proposed and a processing method has been provided when the sparse degree is high.Simulation results demonstrate that the number of traversed reference points is reduced by 70% and the average locating error by 15%,and the positioning accuracy and real-time performance have been effectively improved as compared with the traditional algorithm.

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更新日期/Last Update: 2017-06-16