\section{Related Work}

	Indoor localization based on \docWIFI{} signal strengths dates back to the year
	2000 and the work of Bahl and Padmanabhan \cite{radar}. During an offline-phase, a
	multitude of reference measurements are conducted once. Those measurements are compared
	against live readings during an online-phase. The pedestrian's location is inferred 
	using the $k$-nearest neighbor(s) based on the Euclidean distance between currently
	received signal strengths and the readings during the offline phase.
	
	Inspired by this initial work, Youssef et al. \cite{horus} proposed a more robust, probabilistic 
	approach. Fingerprints were placed every \SI{1.52}{\meter} and estimated by scanning each location 
	100 times. The resulting signal strength propagation for one location is hereafter denoted by a histogram.
	The latter can be compared against live measurements to infer its matching-probability. The center
	of mass among the $k$ highest probabilities, including their weight, describes the pedestrian's current location.
	%
	In \cite{ProbabilisticWlan}, a similar approach is used and compared against nearest neighbor and machine learning.
	Furthermore, they mention potential issues of unseen transmitters and describe a simple heuristic of how to handle such cases.
	
	Meng et al \cite{secureAndRobust} further discuss several fingerprinting issues like environmental changes 
	after the fingerprints were recorded. They propose an outlier detected based on RANSAC to remove potentially
	distorted measurements and thus improve the matching process.
	
	Despite a very high accuracy due to real-world comparisons, all approaches suffer from tremendous setup-
	and maintainance times. 
	
	Therefore it makes sense to replace those time consuming fingerprints by model predictions. 
	Those are a well established research field, mainly used to determine the \docWIFI{}-coverage
	for new installations. \cite{ANewPathLossPrediction, PredictingRFCoverage, empiricalPathLossModel}
	
	
	einfach messen, ab und zu einen GPS fix und danach genetisch alles zuusammenoptimieren. also kein vorwissen
	\cite{WithoutThePain}
	
	das muesste noch was aehnliches sein:
	\cite{crowdinside}
	
	
	
	
	neben signalstärke gibt es noch viele andere methoden über laufzeiten wie beim gps etc.
	diese erfordern meist aber spezial-hardware und laufen deshalb nicht so einfach auf dem smartphone [= ueberleitung!]
	
	
	
 \cite{secureAndRobust}


andere methoden neben signalstärke
\cite{TimeDifferenceOfArrival1} \cite{TOAAOA}

\cite{Ebner-15}
