Floor positioning method indoors with smartphone’s barometer
Floor positioning method indoors with smartphone’s barometer
复制标题
使用智能手机气压计进行室内地板定位方法
DOI：
10.1080/10095020.2019.1631573
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发表时间：
2019-04
影响因子：
6
通讯作者：
Guo Hang
中科院分区：
文献类型：
--
作者：
Yu Min;Xue Feng;Ruan Chao;Guo Hang
本文针对传统地板定位技术存在的可用性低、环境依赖性高的问题，提出了一种基于智能手机气压计的室内地板定位方法。首先，得到了具有“进入”检测算法的初始楼层位置算法。其次，通过气压波动的特征来识别用户的上楼或下楼活动。第三，估算垂直方向上的移动距离和上楼或下楼时的楼层变化，得到准确的楼层位置。为了解决不同手机气压计对楼层误判的问题，本文对不同手机气压数据进行了计算，有效降低了由于手机的非均质性导致的气压估算高程误差。实验结果表明，三种类型的手机地板识别的平均正确率都在85%以上，同时降低了对环境的依赖，提高了可用性。在此基础上，对基于指纹的无线局域网地板定位（WFL）方法、基于神经网络地板定位（NFL）方法和基于磁地板定位（MFL）方法这三种常用的地板定位方法进行了比较分析。实验结果表明，使用华为mate10 Pro手机进行地板识别的正确率达到94.2%。
This paper presents an indoor floor positioning method with the smartphone’s barometer for the purpose of solving the problem of low availability and high environmental dependence of the traditional floor positioning technology. First, an initial floor position algorithm with the “entering” detection algorithm has been obtained. Second, the user’s going upstairs or downstairs activities are identified by the characteristics of the air pressure fluctuation. Third, the moving distance in the vertical direction and the floor change during going upstairs or downstairs are estimated to obtain the accurate floor position. In order to solve the problem of the floor misjudgment from different mobile phone’s barometers, this paper calculates the pressure data from the different cell phones, and effectively reduce the errors of the air pressure estimating the elevation which is caused by the heterogeneity of the mobile phones. The experiment results show that the average correct rate of the floor identification is more than 85% for three types of the cell phones while reducing environmental dependence and improving availability. Further, this paper compares and analyzes the three common floor location methods – the WLAN Floor Location (WFL) method based on the fingerprint, the Neural Network Floor Location (NFL) methods, and the Magnetic Floor Location (MFL) method with our method. The experiment results achieve 94.2% correct rate of the floor identification with Huawei mate10 Pro mobile phone.
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DOI：
10.4028/www.scientific.net/amm.701-702.989
发表时间：
2014-12
期刊：
Applied Mechanics and Materials
影响因子：
--
作者：
W. Yu;Peng Li;Z. Chen;Chang Li
通讯作者：
W. Yu;Peng Li;Z. Chen;Chang Li
DOI：
10.1007/978-3-319-23386-4_10
发表时间：
2017
期刊：
--
影响因子：
--
作者：
J. Pelton;Sergio Camacho-Lara
通讯作者：
J. Pelton;Sergio Camacho-Lara
影响因子：
3.8
作者：
A. W. Tsui;Yu-Hsiang Chuang;Hao-Hua Chu
通讯作者：
A. W. Tsui;Yu-Hsiang Chuang;Hao-Hua Chu
DOI：
10.1109/mass.2014.49
发表时间：
2014-10
期刊：
2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems
影响因子：
--
作者：
Haibo Ye;Tao Gu;Xianping Tao;Jian Lu
通讯作者：
Haibo Ye;Tao Gu;Xianping Tao;Jian Lu
DOI：
10.1109/icccas.2013.6765288
发表时间：
2013-11
期刊：
2013 International Conference on Communications, Circuits and Systems (ICCCAS)
影响因子：
--
作者：
Guowei Zhang;Xu Zhan;Liu Dan
通讯作者：
Guowei Zhang;Xu Zhan;Liu Dan