摘要
随着传感器网络的不断发展,基于位置服务逐步成为研究热点,其中的室内定位技术发展更为迅猛。为准确而快速地确定室内待测节点位置,通过对ZigBee无线网络的研究,提出在离线建库阶段使用基于离群点检测与双阈值滤波算法(频率阈值和均值阈值)处理采集到的不同接入点(Access Point,AP)的信号强度指示(Received Signal Strength Indication,RSSI),建立高精度指纹数据库,然后在线定位阶段,结合使用模糊C均值聚类(Fuzzy C-Means,FCM)和基于频率因子的加权K最邻近算法(Weighting K-Nearest Neighbor,WKNN)计算出待测节点的最终位置,并给出"备用位置"。结果表明,采用该研究方法建立的指纹库在精度上有较大提升,同时定位精度也显著提高。
With the continuous development of sensor networks,location-based services have gradually become the research hotspots,and indoor positioning technology is one of the most which are the fastest developing technology.In order to accurately and quickly position the location of indoor nodes to be measured,this research based on ZigBee wireless network proposes to use the algorithm based on outlier detection and double threshold filtering(frequency threshold and mean threshold)to process the RSSI data,which are from different access points(APs),to establishes a high-precision fingerprint database.Then the final position of the node to be measured is calculated by using fuzzy C-Means(FCM)and the weighted K-nearest neighbor(WKNN)algorithm based on the frequency factor,and the“spare location”is also obtained.The result shows that the fingerprint database established by this research method has a great improvement in accuracy and the positioning accuracy has also been significantly improved.
作者
王培良
张婷
肖英杰
Wang Peiliang;Zhang Ting;Xiao Yingjie(Engineering Research Center of Simulation Technology of the Ministry of Education,Merchant Marine College,Shanghai Maritime University,Shanghai 201306,China;Weifang University of Science and Technology,Weifang 262700,China;Shandong Transport Vocational College,Weifang 261206,China)
出处
《电子技术应用》
2018年第10期97-101,105,共6页
Application of Electronic Technique
基金
国家科技支撑计划子课题(2015BAG20B05-02)
潍坊科技学院2018年度校级课题(科技类)(2018KJYB02,2018KJYB03)
关键词
室内定位
ZIGBEE无线网络
FCM
WKNN
indoor positioning
ZigBee wireless network
fuzzy c-means
weighted k-nearest neighbor
