摘要
针对室内定位中,WiFi位置指纹法存在的定位实时性和精度的问题,提出一种基于改进模糊核聚类(KFCM)和加权K近邻(WKNN)结合的室内定位方法,旨在降低定位时间和改善定位精度。首先利用快速搜索和发现峰值聚类(CFSFDP)确定聚类数目和初始聚类中心,克服KFCM算法对初始聚类中心选取的依赖性而导致聚类结果不稳定的缺点,在此基础上,采用WKNN进行定位匹配,提高定位精度。实验表明,所提出方法相较于无聚类的室内定位方法,能在保证一定精度的前提下,减少定位计算量和时间。此外,将所提出方法与基于K均值、KFCM和CFSFDP的方法进行实验对比,结果显示,该方法具有更好的聚类效果和定位精度。
An indoor positioning method based on kernel fuzzy C-means （KFCM） and weighted K-nearestneighbor （ WKNN） is proposed to reduce the positioning time and improve the positioning accuracy. Firstly, thenumber of clusters and the initial cluster center are determined by clustering by fast search and find of densitypeaks （CFSFDP） to overcome instability, and then WKNN is used for location matching to improve accuracy.The experimental results show that compared with non-clustering indoor localization, the proposed method canreduce the computation time with satisfied accuracy. In addition, compared with the method based on K-means,KFCM and CFSFDP, the proposed method has better clustering effect and positioning accuracy.
作者
杜凯颖
张为公
王东
DU Kai-ying;ZHANG Wei-gong;WANG Dong(School of Instrument Science ＆ Engineering, Southeast University, Nanjing 210096, China)
出处
《测控技术》
CSCD
2018年第2期42-46,共5页
Measurement & Control Technology
关键词
室内定位
模糊核聚类
加权K近邻
快速搜索和发现峰值聚类
indoor location
kernel fuzzy C-means
weighted K-nearest neighbor
clustering by fast search andfind of density peaks
