Indoor Localization of a Moving Mobile Terminal by an Enhanced Particle Filter Method

Dominika Bodzon , Marek Kozak , Michał Okulewicz , Michał Piwowarski , Patryk Tenderenda

Abstract

This article presents a method of localizing a moving mobile terminal (i.e. phone) with the usage of the Particle Filter method. The method is additionally enhanced with the predictions done by a Random Forest and the results are optimized with the usage of the Particle Swarm Optimization algorithm. The method proposes a simple model of movement through the building, a likelihood estimation function for evaluating locations against the observed signal, and a method of generating multiple location propositions from a single point prediction statistical model on the basis of model error estimation. The method uses a data set of the GSM and WiFi networks received signals’ strengths labeled with a receiver’s 3D location. The data have been gathered in a six floor building. The approach is tested on a real world data set and compared with a single point estimation performed by a Random Forest. The Particle Filter approach has been able to improve floor recognition accuracy by around 7% and lower the median of the horizontal location error by around 15%.
Author Dominika Bodzon
Dominika Bodzon,,
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, Marek Kozak
Marek Kozak,,
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, Michał Okulewicz ZSIMO
Michał Okulewicz,,
- Department of Artificial Intelligence and Computational Methods
, Michał Piwowarski
Michał Piwowarski,,
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, Patryk Tenderenda
Patryk Tenderenda,,
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Pages512-522
Publication size in sheets0.5
Book Rutkowski Leszek, Korytkowski Marcin, Scherer Rafal, Tadeusiewicz Ryszard, Zadeh Lotfi A., Zurada Jacek (eds.): ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, (ICAISC 2016), PT II, Lecture Notes in Artificial Intelligence, vol. 9693, 2016, SPRINGER INT PUBLISHING AG, ISBN 978-3-319-39384-1
Keywords in EnglishParticle Filter, Random Forest, Particle Swarm Optimization, Machine learning, Hidden Markov models, On-line mobile phone localization
Abstract in PolishPraca weryfikuje możliwość wykorzystania metody filtra cząsteczkowego w procesie lokalizacji poruszającego się w budynku mobilnego terminala sieci radiowych (np. telefonu komórkowego). Dzięki połączeniu jednopunktowych predykcji uzyskanych lasem losowym, sekwencji predykcji generowanych bazowym filtrem cząsteczkowym z metodą losowego błądzenia w obszarze zadanego budynku oraz poprawie jakości za pomocą optymalizacji rojem cząstek uzyskano poprawę predykcji piętra o 7% oraz zmniejszono medianę błędu lokalizacji w poziomie o 15% w oparciu o dane o sygnałach sieci WiFi.
DOIDOI:10.1007/978-3-319-39384-1_45
URL http://link.springer.com/chapter/10.1007/978-3-319-39384-1_45
Languageen angielski
Score (nominal)15
ScoreMinisterial score = 15.0, 22-06-2017, BookChapterSeriesAndMatConf
Ministerial score (2013-2016) = 15.0, 22-06-2017, BookChapterSeriesAndMatConf
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