摘要
针对蓝牙定位精度差、耗时长等问题,该文提出一种基于区域优选的自适应蓝牙指纹定位算法。离线阶段,采用粗细粒度划分建立关于RSSI采样点位置映射的指纹库;在线阶段,根据信标距离和RSSI的关系,提出加权欧氏距离和区域优选算法,有效地克服了定位稳定性差和耗时长的问题,定位效率提高了40%,在在线定位过程中,采用自适应K值定位算法,有效地剔除了离定位点较远的点,提高了定位的精度与稳定性。在5 m×9 m的区域内进行定位精度测试,结果表明:蓝牙定位平均定位误差为0.92 m,定位误差均在2 m以内,90%的点定位精度优于1.5 m。
Aiming at the problems of poor positioning accuracy and longtime of Bluetooth,this paper proposed an adaptive Bluetooth fingerprint localization algorithm based on region preference.In the offline phase,a fingerprint database for received signal strength indication(RSSI)and sample point location mapping was established using coarse-grained partitioning.In the online phase,according to the relationship between beacon distance and RSSI,the weighted Euclidean distance and region optimization algorithm were proposed,which effectively overcame the problem of poor positioning stability and long time,and the positioning efficiency was improved by 40%.In the online positioning process,the adaptive K-value localization algorithm was used to effectively remove the points far from the positioning point,which improved the accuracy and stability of the positioning.The positioning accuracy test was carried out in the area of 5 m×9 m.The results showed that the average positioning error of Bluetooth positioning was 0.92 m,the positioning error was within 2 m,and the positioning accuracy of 90%was better than 1.5 m.
作者
靳赛州
陈国良
张超
王轩
王俊鹏
王睿
JIN Saizhou;CHEN Guoliang;ZHANG Chao;WANG Xuan;WANG Junpeng;WANG Rui(Key Lab of Land,Environment and Disaster Monitoring,Ministry of Natural Resources,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China;School of Environment Science and Spatial Informatics,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China)
出处
《测绘科学》
CSCD
北大核心
2020年第8期51-56,共6页
Science of Surveying and Mapping
基金
国家重点研发计划项目(2016YFB0502105)
国家自然科学基金项目(41371423)
江苏省自然科学基金项目(BK20161181)
江苏高校品牌专业建设工程资助项目(PPZY2015B144)。
关键词
位置指纹匹配技术
蓝牙定位
加权欧氏距离
区域优选
自适应K值
location fingerprint matching technology
Bluetooth positioning
weighted Euclidean distance
regional preference
adaptive K-value
