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diff --git a/tex/chapters/smoothing.tex b/tex/chapters/smoothing.tex
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--- a/tex/chapters/smoothing.tex
+++ b/tex/chapters/smoothing.tex
@@ -85,9 +85,8 @@ Unlike the transition presented in section \ref{sec:transition}, it is not possi
 Here, $p(\vec{q}_{t+1} \mid \vec{q}_{t})$ needs to provide the probability of the \textit{known} future state $\vec{q}_{t+1}$ under the condition of the current state $\vec{q}_{t}$. 
 In case of indoor localisation using particle filtering, it is necessary to not only provide the probability of moving to a particle's position under the condition of its ancestor, but also of all other particles at time $t$. 
 The smoothing transition model therefore calculates the probability of being in a state $\vec{q}_{t+1}$ in regard to previous states and the pedestrian's walking behaviour. 
-This means that a state $\vec{q}_t$ gets rewarded with a high probability, if it is a proper ancestor (realistic previous position) of a future state $\vec{q}_{t+1}$. 
-%observations von barometer und turn sind ziemlich genau. 
-%of course, instead of the line of side one could choose to calculate the the shortest path. however, this requires a trombendes calculation time and is therefore not further discussed within this work. 
+This means that a state $\vec{q}_t$ is more likely if it is a proper ancestor (realistic previous position) of a future state $\vec{q}_{t+1}$. 
+In the following a simple and inexpensive approach for receiving this information will be described.
 
 By writing
 \begin{equation}