\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
% Finding optimal bandwidth
% Expensive computation
