@article {1532,
	title = {Using Barometer for Floor Assignation within Statistical Indoor Localization.},
	journal = {Sensors (Basel, Switzerland)},
	volume = {23},
	year = {2022},
	month = {2022 Dec 22},
	publisher = {MDPI},
	abstract = {This paper presents methods for floor assignation within an indoor localization system. We integrate the barometer of the phone as an additional sensor to detect floor changes. In contrast to state-of-the-art methods, our statistical model uses a discrete state variable as floor information, instead of a continuous one. Due to the inconsistency of the barometric sensor data, our approach is based on relative pressure readings. All we need beforehand is the ceiling height including the ceiling{\textquoteright}s thickness. Further, we discuss several variations of our method depending on the deployment scenario. Since a barometer alone is not able to detect the position of a pedestrian, we additionally incorporate Wi-Fi, iBeacons, Step and Turn Detection statistically in our experiments. This enables a realistic evaluation of our methods for floor assignation. The experimental results show that the usage of a barometer within 3D indoor localization systems can be highly recommended. In nearly all test cases, our approach improves the positioning accuracy while also keeping the update rates low.},
	keywords = {Humans, Models, Statistical, Pedestrians},
	issn = {1424-8220},
	doi = {10.3390/s23010080},
	author = {Fetzer, Toni and Ebner, Frank and Deinzer, Frank and Grzegorzek, Marcin}
}
