first draf related work finished

tex/chapters/relatedwork.tex

@@ -71,22 +71,8 @@ Finally, as the name recursive state estimation says, it requires to find the mo
 In the discrete manner of a particle representation this is often done by providing a single value, also known as sample statistic, to serve as a best guess \cite{Bullmann-18}. 
 Examples are the weighted-average over all particles or the particle with the highest weight.
 However in complex scenarios like a multimodal representation of the posterior, such methods fail to provide an accurate statement about the most probable state.
-Thus, in \cite{} we present a rapid computation scheme 
-
-A well known solution is KDE. 
-For example \cite{} used a ... in .... However it is obvious that this method has a massive computation time and is thus not practicle for smartphone-based solutions.
-
-Within this paper we use a rapid bla und blub, what was recently presented in \cite{}. 
-
-\todo{umschreiben mit entsprechenden cites und auf particles }
-
-\todo{mal die letzten beiden IPIN Jahre durchstöbern und deren system raussuchen. \\
-dabei vor allem mit dem fokus, nicht sehr flexibel, braucht fertige ap positionen etc draufschauen \\ 
-danach ein wenig schaun, ob es andere gibt die einzelne verfahren, wie wir sie haben ähnlich machen \\
-nicht verbergen das wir hier viel aus unseren eigenen paper zehren, also ruhig citen.}
-
-1/2 bis 3/4 Seite
-
+Thus, in \cite{Bullmann-18} we present a rapid computation scheme of kernel density estimates (KDE).
+Recovering the probability density function using an efficient KDE algorithm yields a promising approach to solve the state estimation problem in a more profound way.
 
 
 

tex/egbib.bib

@@ -2905,3 +2905,14 @@ address = {{Rothenburg, Germany}},
   publisher={Hindawi}
 }
 
+@inproceedings{Bullmann-18,
+	author={Bullmann, Markus and Fetzer, Toni and Ebner, Frank and Grzegorzek, Marcin and Deinzer, Frank},
+	booktitle={21th Int. Conf. on Information Fusion (FUSION)},
+	title={{Fast Kernel Density Estimation using Gaussian Filter Approximation}},
+	year={2018},
+	IGNOREmonth={October},
+	pages={1-8},
+	note={under review}
+}
+
+