摘要
由于人们对基于位置服务的需求越来越高,室内定位技术在诸多领域得到了广泛的应用,而定位算法则是室内定位研究的重点。首先介绍了最近邻和KNN两种信号强度指纹定位算法,并说明了KNN信号强度指纹算法的不足。在KNN信号强度指纹定位算法的基础上,提出了改进的基于区域划分的定位算法。在定位阶段,首先对接收信号强度进行补偿和滤波处理,以降低各种外在因素对定位精度的影响;同时对定位区域进行划分,选择主参考节点,并基于加权的最近邻匹配来选择最近的信号强度指纹;最后对定位结果进行计算并验证。仿真实验表明,改进的区域划分算法相对于传统的KNN算法,定位精度提高了22.2%,达到2.1m,证明了改进算法的可行性。
As people have increasingly high demand of location-based services,indoor positioning technology in many fields has been widely used,and location algorithm is most important in indoor positioning research.This paper described the nearest neighbor and KNN signal strength fingerprint location algorithm and showed the disadvantage of KNN fingerprint algorithm.On the basis of KNN localization algorithm,an improved location algorithm based on region division was proposed.In the first stage,received signal strength was compensated and filtered to reduce the influence of various external factors on the positioning accuracy.Then we divided the location area,selected the major node and the most recent signal strength fingerprints.Finally the location result was calculated and verfied.The simulation proves the improved region division algorithm improves the positioning accuracy of 22.2%,reaching 2.1mcompared with the traditional KNN algorithm,which proves the feasibility of this improved algorithm.
出处
《计算机科学》
CSCD
北大核心
2014年第11期178-181,共4页
Computer Science
关键词
位置指纹
K近邻
区域划分
室内定位
Location fingerprint
KNN
Region division
Indoor positioning
