摘要
在简单介绍动态K值加权室内定位算法(EWKNN)并分析其不足的基础上,探索研究了基于动态K值及AP MAC地址筛选的室内定位算法。该算法首先使用EWKNN方法动态选择参考点个数,并根据测试点和参考点之间AP的MAC匹配度,进一步筛选出最优的定位参考点;最后采用得到的最优参考点与测试点之间的距离进行加权定位。实验表明,相对于传统的EWKNN定位算法,提出的算法具有较高的定位精度。
After describing algorithm for indoor positioning based on dynamic K value and weighted localization（EWKNN）,this article put forward an improved indoor localization algorithm based on AP MAC address match.First of all,EWKNN is used to dynamically choose reference points.Then according to the AP MAC matching degree between reference points and test point,further optimal positioning reference points are determined.Finally the weighted positioning is obtained in terms of signal distance between optimal reference points and test point.Experimental studies indicate that the improved algorithm has better performance at positioning accuracy comparing with EWKNN localization algorithm.
出处
《计算机科学》
CSCD
北大核心
2016年第1期163-165,共3页
Computer Science
基金
国家高技术研究发展计划(863计划):导航与位置服务系统关键技术及应用示范(二期)"特大城市室内外无缝定位信号体制与系统构建"(2013AA12A201)资助
关键词
室内定位
EWKNN
信号强度
位置指纹
Indoor positioning
EWKNN
Signal strength
Location fingerprint
