\begin{abstract}

	Indoor localization and indoor pedestrian navigation is an active field of research 
	with increasing attention.
	%
	As of today, many systems will run on commodity smartphones but most of them still rely on
	fingerprinting, which demands for high setup- and maintenance-times.
	Alternatives, such as simple signal strength prediction models, provide fast setup times,
	but often do not provide the accuracy required for use-cases like indoor navigation or
	location-based services.
	%
	While more complex models provide an increased accuracy by including architectural knowledge 
	about walls and other obstacles, they often require additional computation during runtime and
	demand for prior knowledge during setup.
	
	Within this work we will thus focus on simple, easy to set-up models and evaluate their
	performance compared to real-world measurements. The evaluation ranges from a fully empiric, instant
	setup, given the transmitter locations are well-known, to a highly-optimized scenario based
	on some reference measurements within the building. Furthermore, we will propose a new 
	signal strength prediction model as a combination of several simple ones. This tradeoff
	increases accuracy with only minor additional computations.
	%
	All of the optimized models are evaluated within an actual smartphone-based
	indoor localization system. This system uses the phone's \docWIFI{}, barometer and IMU
	to infer the pedestrian's current location via recursive density estimation based on particle filtering.
	
	We will show that while a \SI{100}{\percent} empiric parameter choice for the model already provides enough
	accuracy for many use-cases, a small number of reference measurements is enough to dramatically increase
	such a system's performance.
	
	
	
	

	%system setup kostet oft sehr viel zeit [fingerprinting kostet]
	%deshalb werden alternativen untersucht:
	%bekannte AP position mit empirischen parametern
	%und optimierung durch einige referenzmessungen

	%floorplan wird für die navigation bzw orientierung des anwenders eh gebraucht
	%dann kann man ihn auch gleich für ein bewegungsmodell nutzen

	%es sollte klar werden, dass es auch darum geht, effizient
	%auf einem normalen smartphone lauffähig zu sein [passend zum journal]

\end{abstract}

% TODO
\begin{CCSXML}

\end{CCSXML}

%\ccsdesc[500]{Computer systems organization~Embedded systems}
%\ccsdesc[300]{Computer systems organization~Redundancy}
%\ccsdesc{Computer systems organization~Robotics}
%s\ccsdesc[100]{Networks~Network reliability}

\keywords{\docWIFI{}, indoor localization, sensor fusion}
