59 lines
2.5 KiB
TeX
Executable File
59 lines
2.5 KiB
TeX
Executable File
\begin{abstract}
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Indoor localization and indoor pedestrian navigation is an active field of research
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with increasing attention.
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%
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As of today, many systems will run on commodity smartphones but most of them still rely on
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fingerprinting, which demands for high setup- and maintenance-times.
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Alternatives, such as simple signal strength prediction models, provide fast setup times,
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but often do not provide the accuracy required for use-cases like indoor navigation or
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location-based services.
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%
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While more complex models provide an increased accuracy by including architectural knowledge
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about walls and other obstacles, they often require additional computation during runtime and
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demand for prior knowledge during setup.
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Within this work we will thus focus on simple, easy to set-up models and evaluate their
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performance compared to real-world measurements. The evaluation ranges from a fully empiric, instant
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setup, given the transmitter locations are well-known, to a highly optimized scenario based
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on some reference measurements within the building. Furthermore, we will propose a new
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signal strength prediction model as a combination of several simple ones. This tradeoff
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increases accuracy with only minor additional computations.
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%
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All of the optimized models are evaluated within an actual smartphone-based
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indoor localization system. This system uses the phone's \docWIFI{}, barometer and IMU
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to infer the pedestrian's current location via recursive density estimation based on particle filtering.
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We will show that while a \SI{100}{\percent} empiric parameter choice for the model already provides enough
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accuracy for many use-cases, a small number of reference measurements is enough to dramatically increase
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such a system's performance.
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%system setup kostet oft sehr viel zeit [fingerprinting kostet]
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%deshalb werden alternativen untersucht:
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%bekannte AP position mit empirischen parametern
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%und optimierung durch einige referenzmessungen
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%floorplan wird für die navigation bzw orientierung des anwenders eh gebraucht
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%dann kann man ihn auch gleich für ein bewegungsmodell nutzen
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%es sollte klar werden, dass es auch darum geht, effizient
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%auf einem normalen smartphone lauffähig zu sein [passend zum journal]
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\end{abstract}
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% TODO
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\begin{CCSXML}
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\end{CCSXML}
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%\ccsdesc[500]{Computer systems organization~Embedded systems}
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%\ccsdesc[300]{Computer systems organization~Redundancy}
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%\ccsdesc{Computer systems organization~Robotics}
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%s\ccsdesc[100]{Networks~Network reliability}
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\keywords{\docWIFI{}, indoor localization, sensor fusion}
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