摘要
WiFi由于应用广泛而被作为室内定位的热门技术之一,而定位精度与速度向来是研究的焦点.文中针对室内感兴趣区域(ROI)的定位问题,提出了一种证据理论K近邻(EKNN)算法.首先以接收信号强度指示作为指纹,在各区域分别建立无线指纹数据库作为识别的类;然后利用证据理论在各类别内进行近邻证据组合、类别间进行证据融合;最后确定目标所在ROI类,并在类中进行精定位.与其他算法相比,文中设计的EKNN算法的最佳区域类识别率可以达到97%,最大定位误差约为2.2 m,定位效率也有较大提高.
Since WiFi is widely used,it is selected as one of the popular technologies for indoor positioning. The positioning accuracy and speed have always been hot research issues. In this paper,an evidence K-nearest neighbor( EKNN) algorithm is proposed for the localization of the region of interest( ROI). In the algorithm,first,by taking a received signal strength indicator as the fingerprint,a wireless fingerprint database is established as a recognition class in each region. Then,based on the evidence theory,the neighbor evidences within each class are combined and the combined results among the classes are fused. Finally,the ROI class of the target is determined,and a precise positioning is performed within this class. As compared with other algorithms,the EKNN algorithm can achieve a recognition rate of 97% at best and a maximum positioning error of about 2. 2 meters as well as a great positioning efficiency improvement.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2017年第10期87-92,99,共7页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(61471174)
广州市科技计划项目(2014J4100247)
华南理工大学兴华人才项目(J2RS-D6161650)~~
