\section{Introduction}

	State of the art indoor localization systems use a fusion of multiple
	(smartphone) sensors to infer the pedestrian's current location within a building
	based on a variety of sensor observations.
	%
	Among those, the internal IMU, namely accelerometer and gyroscope, is often
	used as a core component, that provides accurate relative movement information
	like step- and turn-detection. However, this requires the pedestrian's
	initial position to be well known, e.g. using a GPS-fix just before
	entering the building. Additionally, the sensor's error will sum up over
	time \cite{Koeping14}.

	Depending on the used fusion-method, latter can be addressed
	using a movement model for the pedestrian, that prevents unlikely movements
	and locations. However, this will obviously work only to some extent and still
	requires the initial position to be at least vaguely known.
	%
	Thus, indoor localization systems incorporate the knowledge of sensors,
	that provide absolute location information, like \docWIFI{} and
	\docIBeacon{}s. The signal strength of nearby transmitters, received
	by the smartphone, yields a vague information about the distance
	towards it. While the provided accuracy is relatively low,
	it can be stabilized by the IMU and vice versa.
	
	
	The downside of this approach is that both, \docWIFI{} and \docIBeacon{}s, require additional prior
	knowledge to work. To infer the probability of the pedestrian currently
	residing at an arbitrary location, the signal strengths received
	by the smartphone are compared with the signal strengths which should be received at this
	location (prior knowledge). As radio frequency (RF) signals are highly dependent 
	on the surroundings, those values can change rapidly within meters.
	%
	That is why fingerprinting became popular, where the required prior knowledge 
	is gathered by manually scanning each location within the building e.g.
	using cells of $\approx \SI{2}{\meter}$ in size. This usually leads to
	a very high accuracy due to actual measurements of the real situation.
	However, the amount of work for the initial
	setup and the maintenance, when transmitters are changed or renovations take
	place, is very high.
	
	Setup- and maintenance effort can be prevented by using models to predict
	the signal strengths that should be received at some arbitrary location.
	Depending on the used model, only a few parameters and the locations of the 
	transmitters within the building are required. For newer installations 
	the latter is often available and tagged within the building's floorplan.
	%As signals are attenuated by the buildings architecture like walls and floors,
	%advanced models additionally include the floorplan within their prediction.
	Obviously, simple models will represent the real signal strengths only
	to some extent, as not all ambient conditions, such as walls, are considered.
	Furthermore, the choice of the model's parameters depends on the actual architecture and \docWIFI{} setup:
	Parameters that work within building A might not work out within building B.
	
	Thus, a compromise comes to mind: Instead of using several hundreds of fingerprints,
	a few reference measurements used for a model setup might be a valid tradeoff
	between resulting accuracy and necessary setup time.
	
	Within this work we will focus on simple signal strength prediction models
	that do not incorporate knowledge of nearby walls, but can be used
	for real-time applications on commercial smartphones.
	%
	To mitigate the issues of those signal strength predictors, we propose a new model
	that is a combination of several simple ones. It is more accurate, requires only minor
	additional computations and thus is well suited for use in mobile applications.
	%
	The to-be-expected accuracy (in \decibel{} and \meter{}) of all models is analyzed for various setups ranging from
	just empirical parameters (no setup time when transmitter positions are known) to optimized
	parameters, where no prior knowledge is necessary and a few reference measurements suffice.
	
	Besides analyzing the \docWIFI{} performance on its own, we will also have
	a closer look at the resulting performance-changes within a fully featured smartphone-based
	indoor localization system using a movement model based on the building's floorplan,
	together with various other sensors and recursive state estimation based on a particle filter.

	%\todo{
	%fokus:\\
	%- wlan parameter + optimierung\\
	%- evaluation der einzel und gesamtergebnisse
	%}

	%\todo{
	%contribution?:\\
	%- neues wifi modell,\\
	%- neues resampling,\\
	%- model param optimierung + eval was es bringt
	%}