基于测量报告信号聚类的指纹定位方法

Fingerprint positioning method based on measurement report signal clustering

针对基于加权K最近邻(WKNN)和机器学习算法的指纹库定位方法存在精度和定位效率较低的问题,提出一种基于测量报告(MR)信号聚类的指纹定位方法。首先,把MR信号分为室内、道路和室外这3种属性;其次,利用地理信息系统(GIS)信息将栅格分为建筑物、道路和室外子区域,并将不同属性的MR数据落入对应的属性子区域;最后,借助K均值(K-Means)聚类算法对栅格内的MR信号进行聚类分析,以创建子区域下的虚拟子区域,并采用WKNN算法对MR测试样本进行匹配。此外,利用欧氏距离计算平均定位精度,并通过生产环境的一些MR数据测试了所提方法的定位性能。实验结果表明,所提方法的50 m定位误差占比为71.21%,相较于WKNN算法提升了2.64个百分点;平均定位定位误差为44.73 m,相较于WKNN算法降低了7.60 m。所提方法具备良好的定位精度和效率,可满足生产环境中MR数据的定位需求。

Aiming at the problems of low positioning precision and efficiency of fingerprint positioning methods based on Weighted K-Nearest Neighbor(WKNN)and machine learning algorithms,a fingerprint positioning method based on Measurement Report(MR)signal clustering was proposed.Firstly,MR signals were divided into three attributes:indoor,road and outdoor.Then,by using the Geographic Information System(GIS)information,the grids were divided into building,road and outdoor sub-regions,and MR data with different attributes were placed in the sub-regions with corresponding attributes.Finally,with the help of K-Means clustering algorithm,MR signals in the grid were clustered and analyzed to create virtual sub-regions under the sub-region,and WKNN algorithm was used to match MR test samples.Besides,the average positioning accuracy was calculated by using the Euclidean distance,and the positioning performance of the proposed method was tested by some MR data in the production environment.Experimental results show that the proportion of 50 m positioning error of the proposed method is 71.21%,which is 2.64 percentage points higher than that of WKNN algorithm,and the average positioning error of the proposed method is 44.73 m,which is 7.60 m lower than that of WKNN algorithm.It can be seen that the proposed method has good positioning precision and efficiency,and can meet the positioning requirements of MR data in the production environment.