\section{Component Description}
	
	As described above, our indoor localisation solely uses the sensors provided by almost each commodity smartphone. 
By assuming statistical independence of all sensors, the probability density of the state evaluation of eq. \eqref{eq:recursiveDensity} is given by 
	%
	\begin{equation}
		%\begin{split}
			p(\vec{o}_t \mid \vec{q}_t) = 
			p(\vec{o}_t \mid \vec{q}_t)_\text{baro} 
			\,p(\vec{o}_t \mid \vec{q}_t)_\text{ib}
			\,p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}
			\enspace.
		%\end{split}
		\label{eq:evalBayes}
	\end{equation}
	%
	Here, every single component refers to a probabilistic sensor model. 
	The barometer information is evaluated using $p(\vec{o}_t \mid \vec{q}_t)_\text{baro}$,
	whereby absolute position information is given by $p(\vec{o}_t \mid \vec{q}_t)_\text{ib}$ for
	\docIBeacon{}s and by $p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}$ for \docWIFI{}. 
	
  Compared to other state-of-the-art systems, step- and turn-detection are not incorporated into the evaluation step. 
  In our approach it stabilizes and improves the sampling of states $\vec{q}$ into moving more realistically. The transition step is the carried out using random walks on a graph, which is built offline, and uses the building's floorplan \cite{ebner-16}. 

	
	\input{chapters/barometer.tex}
	\input{chapters/wifi.tex}
	\input{chapters/stepturn.tex}
	\input{chapters/graph.tex}
	\input{chapters/smoothing.tex}
