Abstract
ABSTRACTThis 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.
Highlights
Location-based services for the products and applications, such as car navigator, the Baidu map and the Didi taxi, have become an indispensable part of the people’s life (Guo et al 2019; Wang, Wu, and Wu 2016; Sadana et al 2011)
A number of WiFi signal sources have been deployed in the building in advance, and the WiFi signal receivers used in the experiment are Huawei Mate8, Xiaomi (Millet) 5S, and Samsung Note2
This paper compares and analyzes three common floor location methods from the view of availability and accuracy, namely the WLAN Floor Location (WFL) method based on the fingerprint, the Neural Network Floor Location (NFL) methods, and the magnetic Floor Location (MFL) method
Summary
Location-based services for the products and applications, such as car navigator, the Baidu map and the Didi taxi, have become an indispensable part of the people’s life (Guo et al 2019; Wang, Wu, and Wu 2016; Sadana et al 2011). People’s demand for the indoor accurate location service is tremendous (Gao et al 2019), such as firefighting and rescues to locate the accurate location of the trapped persons and the firefighters, navigation service in shopping malls, the intelligence of hospital care, and finding a room or facility’s location in a building (Joseph and Sergio 2013; Zhang, Zhan, and Dan 2013; Uradzinski et al 2017) For those indoor positioning scenes, the information of the floor has played an important role (Liu and Zhang 2003; Yu 2015). The user can utilize the mobile station to observe the elevation value, the air pressure value and the temperature value of the measuring point, combine the air pressure value and the temperature value at the measuring point of the receiver, and use the
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