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+\abstract{%
+	%
+	Indoor localization and indoor pedestrian navigation is an active field of research 
+	with increasing attention.
+	%
+	As of today, many systems will run on commercial 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.
+	%
+}