基于聚类和K近邻算法的井下人员定位算法
Underground personnel positioning algorithm based on clustering and K-nearest neighbor algorithm
【索引】莫树培,唐琎,汪郁,等.基于聚类和K近邻算法的井下人员定位算法[J].工矿千亿国际app下载,2019,45(4):43-48.
【Reference】MO Shupei,TANG Jin,WANG Yu,et al.Underground personnel positioning algorithm based on clustering and K-nearest neighbor algorithm[J].Industry and Mine Automation,2019,45(4):43-48.
【DOI】10.13272/j.issn.1671-251x.2018110072
【作者】莫树培1,2,唐琎2,汪郁1,赖普坚2,金礼模1
【Author】 MO Shupei1,2,TANG Jin2,WANG Yu1,LAI Pujian2,JIN Limo1
【作者机构】1.贵州工业职业技术学院 图书与信息中心, 贵州 贵阳551400;2.中南大学 信息科学与工程学院, 湖南 长沙410083
【Unit】1.Book and Information Center, Guizhou Industry Polytechnic College, Guiyang 551400, China;2.School of Information Science and Engineering, Central South University, Changsha 410083, China
【摘要】针对现有基于指纹模的井下定位算法存在的计算量大、实时性低、定位精度较低的问题,提出了基于聚类和K近邻算法的井下人员定位算法。用二分k-means聚类算法对采集的RSSI数据进行分类,建立离线指纹数据库;无线移动终端和动态修正器实时采集RSSI值,分别存储到在线定位数据库和动态修正数据库;根据待测点和动态修正器的离线数据和实时数据,采用软硬件动态修正加权K近邻算法计算权重值,结合离线指纹数据库中待测点的物理位置信息估算其实时位置。实验分析结果表明,所提定位算法的最小标准误差为0.46 m,最大标准误差为3.26 m,平均误差为1.62 m。对比分析结果表明,与未进行聚类分析的算法相比,本文算法的精度更高,实时性更好;与未动态修正权重值的算法相比,本文算法的运算时间略有增加,但定位精度提高了37.21%。
【Abstract】In view of problems of large amount of calculation, low real-time performance and low positioning accuracy of existing fingerprint-based underground positioning algorithm, underground personnel positioning algorithm based on clustering and K-nearest neighbor algorithm was proposed. Bisecting k-means clustering algorithm is used to classify collected RSSI data to establish an offline fingerprint database. Real time RSSI values are collected by wireless mobile terminal and dynamic corrector and stored in online positioning database and dynamic correction database respectively. According to offline data and real-time data, weight value is calculated using software and hardware dynamic correction weighted K-nearest neighbor algorithm, and real-time position is estimated by combining the physical location information of the point to be measured in the offline fingerprint database. The example analysis results show that the minimum standard error of the proposed positioning algorithm is 0.46 m, the maximum standard error is 3.26 m, and the average error is 1.62 m. The results of comparative analysis show that the proposed algorithm has higher precision and better real-time performance than the algorithm without clustering analysis. Compared with the algorithm without dynamic correction of weights, the computation time of the proposed algorithm is slightly increased, but the positioning accuracy is increased by 37.21%.
【关键词】 井下人员定位; 指纹定位; 二分k-means聚类算法; 软硬件动态修正加权K近邻算法; 动态修正
【Keywords】Underground personnel positioning; fingerprint positioning; bisecting k-means clustering algorithm; software and hardware dynamic correction weighted K-nearest neighbor algorithm; dynamic correction
【文献出处】工矿千亿国际app下载,2019年4期
【基金】贵州省科技厅项目(黔科合LH字〔2016〕7069);贵州工业职业技术学院校级科研课题(2018009)
【分类号】TD655.3
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