This paper presents a novel method for detecting the floor level of a smartphone user in an indoor environment based on the atmospheric pressure measured with sensors in the user's smartphone. Atmospheric pressure is one of the most informative factors when estimating altitude, because its relative variation has a strong correlation with changes in altitude. However, it is difficult to directly estimate the altitude based on an observation of the absolute pressure via smartphones, owing to individual differences in the sensors and changes in climate. In this paper, we propose a robust method for estimating the floor level by decomposing the observed pressure into three components. The first component is the change in altitude, and this component is the primary measure. The second is the global variation, such as changes in climate. The third concerns device-dependent variation. We utilize localization infrastructure, such as beacons or Wi-Fi access points, sparsely located in the environment, because such localization mechanisms can provide floor-level information. Exploiting this mechanism, time-series references to the pressure on each floor level can be generated collectively by aggregating readings from the pressure sensors of multiple users. Using these as a reference, each output of the pressure can be decomposed into device-specific offsets, environmental trends, and the altitude-dependent component. By extracting the altitude-dependent component, the floor level of the user can be estimated robustly without relying on fixed observations of atmospheric pressure.
['Ryosuke Ichikari', 'Luis Carlos Manrique Ruiz', 'Masakatsu Kourogi', 'Takeshi Kurata', 'Tomoaki Kitagawa', 'Sota Yoshii']
Indoor floor-level detection by collectively decomposing factors of atmospheric pressure