摘要
针对目前大多数楼层识别方法由于未考虑楼梯间的活动识别,在楼梯处楼层定位结果来回切换的问题,提出了一种基于Wi-Fi/气压计组合的楼层定位方法。在平面楼层离线建立指纹库,并用基于密度聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对指纹库进行聚类,区分不同区域的信号特征,在线接收信号与指纹库进行匹配识别楼层。在楼梯间用气压计进行上下楼活动识别,活动识别融合了步频检测,以应对突然运动状态的改变导致的误判。结果显示,在平面楼层本文方法较基本楼层识别方法准确率提高;在楼层过渡部分能有效识别上下楼活动,解决了楼梯间楼层来回切换问题,融合步频检测后能有效剔除人运动状态改变导致的误判。实验表明本文的楼层定位方法能有效应对复杂环境的楼层定位需求,且完备性较强。
In view of the fact that most floor identification methods currently do not consider the activity recognition of up and down stairs resulting in the floor identification results switch back and forth in the stairwell,a floor identification method was proposed based on Wi-Fi/barometer combination.In the offline phase the fingerprint database was established on the plane floor,and the fingerprint database was clustered by the density-based spatial clustering of applications with noise(DBSCAN)algorithm to distinguish the signal characteristics of different regions.In the online phase the receiving signal and the fingerprint database are matched to identify floor.In the stairwell,the barometer is used to identify the activities of the upper and lower stairs.The activity recognition incorporates the step frequency detection to cope with the misjudgment caused by the sudden change of the motion state.The results show that the accuracy of the method of this paper is obviously improved compared with the basic floor recognition method in the plane floor.The floor transition can effectively identify the up and down stairs activities,and solve the problem of the switching between floors in the stairwell.After the step frequency detection is incorporated,the misjudgment caused by the sudden change of the motion state can be effectively eliminated.Experiments show that the floor identification method of this paper can effectively deal with the floor identification requirements of complex environments,and the completeness is strong.
作者
司明豪
汪云甲
孙猛
王睿
SI Ming-hao;WANG Yun-jia;SUN Meng;WANG Rui(School of Environment Science and Spatial Information,China University of Ming and Technology,Xuzhou 221116,China)
出处
《科学技术与工程》
北大核心
2019年第33期236-243,共8页
Science Technology and Engineering
基金
国家重点研发计划项目(2016YFB0502102)资助
关键词
室内定位
楼层识别
信号指纹
气压
基于密度聚类
活动识别
indoor position
floor identification
wireless fingerprinting
barometric pressure
DBSCAN
activity recognition
