摘要
针对行人室内定位过程中高程出现漂移的问题,本文提出了一种基于楼层匹配的启发式高程约束室内定位算法。该算法以传统的足联式行人航位推算为基础,根据行人在室内平面行走时,任意足部与地面接触的时刻高程相等且等于所在平面高度的事实实现。使用平面运动检测算法准确地区分行人在平面的运动,匹配对应楼层的真实高度以求出误差改正数,通过拓展卡尔曼滤波对平面上每一步的高程进行约束。实验结果表明:高差步长与高程标准差组合的平面运动检测算法可以准确地检测平面运动,检测准确率为99%;基于楼层匹配的启发式高程约束室内定位算法可以消除足部着地时与真实楼层高度间的高程偏差,显著提升高程定位精度。
Aiming at solving the problem of height drift in pedestrian indoor positioning process, this paper proposes a heuristic height constraint indoor positioning algorithm by using floor matching. The algorithm is conducted based on the traditional foot-mounted pedestrian dead reckoning and the fact that when a pedestrian walks on an indoor plane, the heights at which foot touches the ground are same and equal to the height of corresponding plane. A plane motion detection algorithm is used to accurately distinguish the movement of pedestrian in the plane, to selecte the real height of corresponding plane to find the error correction number, and to constrain the elevation of each step in the plane by extending the Kalman filter. Experimental results show that the plane motion detection algorithm combined with the height difference step and elevation standard deviation can accurately detect the plane motion, and the detection accuracy rate is 99%. The heuristic height constraint indoor positioning algorithm based on floor matching can eliminate the foot landing elevation deviation from the real floor height, and significantly improves the elevation positioning accuracy.
作者
梁建
王坚
LIANG Jian;WANG Jian(School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou,Jiangsu 221116, China;NASG Key Laboratory for Land Environment and Disaster Monitoring, Xuzhou,Jiangsu 221116, China;School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)
出处
《中国科技论文》
CAS
北大核心
2018年第15期1805-1810,共6页
China Sciencepaper
基金
国家重点研发计划专项(2016YFC0803103)
关键词
行人航位推算
楼层匹配
启发式高程约束
运动状态检测
室内定位
pedestrian dead reckoning
floor matching
heuristic height constraint
motion state detection
indoor position
