Hybrid algorithm for floor detection using GSM signals in indoor localisation task

Rafał Górak , Marcin Luckner

Abstract

One of challenging problems of indoor localisation based on GSM fingerprints is the detection of the current floor. We propose an off--line algorithm that labels fingerprints with the number of current floor. The algorithm uses one pass through the given route to learn the GSM fingerprints. After that the height on the testing passes of the same route can be estimated with high accuracy even for measures registered with various velocities and a month after the learning process. The two phase algorithm detects the points of a potential floor change. Next, the regression function normalises height of the change and calculates its direction. The obtained results are up to 40 percent better than the results obtained by the pure regression.
Author Rafał Górak ZPG
Rafał Górak,,
- Department of Foundations Geometry
, Marcin Luckner ZZIMN
Marcin Luckner,,
- Department of Applied Computer Science and Computation Methods
Pages730-741
Publication size in sheets0.55
Book Martínez-Álvarez Francisco, Troncoso Alicia, Quintián Héctor, Corchado Emilio (eds.): Hybrid Artificial Intelligent Systems, Lecture Notes in Artificial Intelligence, vol. 9648, 2016, SPRINGER-VERLAG BERLIN, ISBN 978-3-319-32033-5
Keywords in PolishIndoor Localisation, Fingerprinting, GSM Localisation, Floor detection
Abstract in PolishZaproponowano algorytm działający offline, który przypisuje wektorom sił sygnałów GSM wysokość na jakiej zostały one zebrane. Dwufazowy algorytm najpierw wykrywa punkty zmiany wysokości, a potem estymuje kierunek i wielkość zmiany. Wykorzystując dane z procesu regresji wyniki są normalizowane, aby uzyskać ostateczną estymację. Algorytm daje skuteczność o 40 punkty procentowe lepszą niż sama regresja.
DOIDOI:10.1007/978-3-319-32034-2_61
URL http://link.springer.com/chapter/10.1007/978-3-319-32034-2_61
Languageen angielski
Score (nominal)15
ScoreMinisterial score = 15.0, 23-06-2017, BookChapterSeriesAndMatConf
Ministerial score (2013-2016) = 15.0, 23-06-2017, BookChapterSeriesAndMatConf
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