Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (17): 56-61.DOI: 10.3778/j.issn.1002-8331.1804-0001

Previous Articles     Next Articles

Local updating fingerprint localization algorithm based on region partition

YANG Bin, LI Deng’ao, ZHAO Jumin   

  1. College of Information and Computer, Taiyuan University of Technology, Jinzhong, Shanxi 030600, China
  • Online:2018-09-01 Published:2018-08-30


杨  斌,李灯熬,赵菊敏   

  1. 太原理工大学 信息与计算机学院,山西 晋中 030600

Abstract: In order to solve the problem of high updating cost of indoor positioning fingerprint database, a RFID(Radio Frequency Identification) indoor positioning algorithm is designed to update the fingerprint database locally by region division. The algorithm divides the fingerprint map into several sub-regions by clustering algorithm, which selects a representative point for each sub-region to represent the fingerprint validity of the sub-region, and performs Weighted k-Nearest Neighbor(WkNN) algorithm or locally updates for sub-region database according to detecting the validity of all representative points. The experimental results show that the proposed algorithm can greatly improve the positioning accuracy and long-term positioning stability under the condition of low cost.

Key words: indoor positioning, fingerprint database, Radio Frequency Identification(RFID), clustering algorithm, Weighted k-Nearest Neighbor(WkNN)

摘要: 针对室内定位指纹数据库更新成本过高的问题,设计了一种通过区域划分进行局部更新指纹数据库的RFID(Radio Frequency Identification,射频识别技术)室内定位算法。该算法通过聚类算法将指纹地图分成若干个子区域,每个子区域选取一个代表点代表该子区域的指纹有效性,通过检测代表点的有效性来选择加权k近邻算法(Weighted k-Nearest Neighbor,WkNN)定位或子区域数据库的局部更新。实验结果表明,该算法在低成本的条件下极大限度地提高了定位精度和长期定位稳定性。

关键词: 室内定位, 指纹数据库, 射频识别技术(RFID), 聚类算法, 加权k近邻算法(WkNN)