working on introduction

tex/chapters/introduction.tex

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 \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