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 neigh- bor(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 deter- mined,and a precise positioning is performed within this class.As compared with other algorithms,the EKNN al- gorithm 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.