From e80ae9282afcbea294567e35b957229cb1688e69 Mon Sep 17 00:00:00 2001
From: toni <toni@localhost.localdomain>
Date: Tue, 6 Feb 2018 17:42:06 +0100
Subject: [PATCH] working on introduction

---
 tex/chapters/introduction.tex | 42 +++++++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)

diff --git a/tex/chapters/introduction.tex b/tex/chapters/introduction.tex
index 84529e9..8c64486 100644
--- a/tex/chapters/introduction.tex
+++ b/tex/chapters/introduction.tex
@@ -1,5 +1,47 @@
 \section{Introduction}
 
+Sensor fusion approaches are often based upon probabilistic descriptions like particle filters, using samples to represent the distribution of a dynamical system. 
+To update the system recursively in time, probabilistic sensor models process the noise measurements and a state transition function provides the system's dynamics. 
+Therefore a sample or particle is a representation of one possible system state, e.g. the position of a pedestrian within a building. 
+In most real world scenarios one is then interested in finding the most probable state within the state space.  
+In the discrete manner of the sample representation this is often done by 
+
+%interested in the most proper state within the state space of the dynamic system
+%echte antwort computationel complex deswegen %weighted-average -> problem multimodal; sample mit höhsten wert -> springt viel rum 
+%-> Density -> KDE
+
+%Egal auf welchem Weg das sample set entstanden ist, am ende muss ein verwertbarer wert rauskommen. irgendein 
+
+After calculating 
+
+
+
+In real world scenarios 
+
+
+%find the state that describs our probleme the best 
+%
+
+%  ... in many real world scenarios an estimate of the problem state is required e.g. the position of a pedestrian within a building... 
+%this is often done by calculating the weighted-average of all samples or 
+
+%however multimodalities. 
+
+% in the optimal case 
+
+bessere entscheidung kde raus machen, als einfach nur 
+
+to receive this information 
+
+based upon a set of descrete samples
+
+%for this purpose parameteric estimators like ... are often used in real time scenarios because of their low complexity and short computatinal time.
+
+% however, 
+non parameteric estimators like kde 
+
+ 
+
 \cite{Deinzer01-CIV}
 % KDE wellknown nonparametic estimation method
 % Flexibility is paid with slow speed