diff --git a/tex/bare_conf.tex b/tex/bare_conf.tex
index aee9df3..067f4ae 100644
--- a/tex/bare_conf.tex
+++ b/tex/bare_conf.tex
@@ -148,6 +148,15 @@
 		W\"urzburg, Germany\\
 		\{frank.ebner, toni.fetzer, frank.deinzer\}@fhws.de\\		
 	}
+	\and
+	
+	\IEEEauthorblockN{Marcin Grzegorzek}
+	\IEEEauthorblockA{%
+		Pattern Recognition Group \\
+		University of Siegen\\
+		Siegen, Germany\\
+		\{marcin.grzegorzek\}@uni-siegen.de
+	}%
 }
 
 % conference papers do not typically use \thanks and this command
diff --git a/tex/chapters/abstract.tex b/tex/chapters/abstract.tex
index 25f947a..ee191fe 100644
--- a/tex/chapters/abstract.tex
+++ b/tex/chapters/abstract.tex
@@ -6,6 +6,6 @@ In order to create such paths, we present a method that assigns an importance-fa
 The human movement is then modelled by moving along adjacent nodes into the most proper walking-direction. 
 To enable 3D localisation, realistically shaped stairs for step-wise floor changes are used. 
 The position is estimated over multiple floors integrating different sensor modalities, namely Wi-Fi, iBeacons, barometer, step- and turn-detection.
-The system was tested by omitting any time-consuming calibration process and starts with a uniform distribution instead of a well known pedestrian location. 
+The system was tested by omitting any time-consuming fingerprinting and calibration process and starts with a uniform distribution over the whole building instead of a well known pedestrian location. 
 The evaluation shows that adding prior knowledge is able to improve the localisation, even under unpredictable behaviour, faulty measurements and poorly chosen system parameters. 
 \end{abstract}
diff --git a/tex/chapters/relatedwork.tex b/tex/chapters/relatedwork.tex
index 8f644fd..f4a1501 100644
--- a/tex/chapters/relatedwork.tex
+++ b/tex/chapters/relatedwork.tex
@@ -69,7 +69,7 @@ We introduce a similar approach for square-shaped grids.
 All this allows a wide range of possibilities for modelling the pedestrian's movement, while only sampling valid locations. 
 In virtual environments like video games and simulations, the human motion is often modelled using graphs and path finding techniques. 
 Here, the goal is not only to provide a shortest path, but also the least-cost path, most natural path or least-dangerous path. 
-For example, \cite{Bandi2000} uses an A* algorithm to search a 3D gridded environment for the shortest path to a goal. 
+For example, \cite{Bandi2000} uses an A*-algorithm to search a 3D gridded environment for the shortest path to a goal. 
 An additional smoothing procedure is performed to make the path more natural. 
 They are considering foot span, body dimensions and obstacle dimensions when determining whether an obstacle is surmountable. 
 However, many of this information is difficult to ascertain in real-time or imply additional effort in real-world environments. 
diff --git a/tex/chapters/system.tex b/tex/chapters/system.tex
index ee2ea48..adf3207 100644
--- a/tex/chapters/system.tex
+++ b/tex/chapters/system.tex
@@ -8,7 +8,7 @@
 		\begin{array}{ll}
 			&p(\mStateVec_{t} \mid \mObsVec_{1:t}) \propto\\ 
 				&\underbrace{p(\mObsVec_{t} \mid \mStateVec_{t})}_{\text{evaluation}}
-				\int \underbrace{p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t})}_{\text{transition}}
+				\int \underbrace{p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})}_{\text{transition}}
 				\underbrace{p(\mStateVec_{t-1} \mid \mObsVec_{1:t-1})d\vec{q}_{t-1}}_{\text{recursion}} \enspace,
 		\end{array}
 		\label{equ:bayesInt}
@@ -22,11 +22,11 @@
 	\end{equation}
 	%
 	where $x, y, z$ represent the position in 3D space, $\mObsHeading$ the user's heading and $\mStatePressure$ the
-	relative pressure prediction in hectopascal (hPa). 
-	The recursive part of the density estimation contains all information up to time $t$.
+	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 models the pedestrian's movement as described in section \ref{sec:trans}. 
 	%It should be noted, that we also include the current observation $\mObsVec_{t}$ in it.
-	Differing from the usual notation, the state transition also includes the current observation $\mObsVec_{t}$ \cite{Koeping14}.
+	As \cite{Koeping14-PSA} has proven, we are able to include the observation $\mObsVec_{t-1}$ into the state transition. 
 
 	Containing all relevant sensor measurements to evaluate the current state, the observation vector is defined as follows:
 	%