intro component description

competition/tex/chapters/introduction.tex

@@ -32,8 +32,7 @@ where $\mObsVec_{1:t} = \mObsVec_{1}, \mObsVec_{1}, ..., \mObsVec_{t}$ is a seri
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 	where $x, y, z$ represent the position in 3D space, $\mStateHeading$ the user's heading and $\mStatePressure$ the relative atmospheric pressure prediction in hectopascal (hPa). 
 	The recursive part of the density estimation contains all information up to time $t-1$.
-	Furthermore, the state transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$ models the pedestrian's movement and is carried out using random walks on a graph, which is built offline, and uses the building's floorplan \cite{ebner-16}. 
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+	Furthermore, the state transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$ models the pedestrian's movement, whereby the evaluation provides a likelihood for every sensor.
 	Containing all relevant sensor measurements to evaluate the current state, the observation vector is defined as follows:
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 	\begin{equation}