first draft related work

tex/chapters/relatedwork.tex

@@ -29,14 +29,33 @@ In recent years, smoothing gets attention mainly in other areas as indoor locali
 The early work of \cite{isard1998smoothing} demonstrates the possibilities of smoothing for visual tracking.
 They used a combination of the CONDENSATION particle filter with a forward-backward smoother. 
 Based on this pioneering approach, many different solutions for visual and multi-target tracking have been developed \cite{Perez2004}.
-For example, in \cite{Platzer:2008} a particle smoother is used to reduce multimodalities in a blood flow simulation for human vessels. Or \cite{} 
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-Nevertheless, their are some promising approach for indoor localisation systems as well. For example ...
+For example, in \cite{Platzer:2008} a particle smoother is used to reduce multimodalities in a blood flow simulation for human vessels. Or \cite{Hu2014} uses a smoother to overcoming the problem of particle impoverishment while predicting the Remaining Useful Life (RUL) of equipment (e.g. a Lithium-ion battery).
 
 %smoothing im bezug auf indoor
-Smoothing solutions in indoor localisation werden bisher nicht wirklich behandelt.  das liegt hauptsächlich daran das es sehr langsam ist \cite{}. es gibt ansätze von ... und ... diese benutzen blah und blah. wir machen das genauso/besser.
+Nevertheless, their are some promising approaches for indoor localisation systems as well. 
+For example \cite{Nurminen2014} deployed a fixed-interval forward-backward smoother to improve the position estimation for non-real-time applications. 
+They combined Wi-Fi, step and turn detection, a simple line-of-sight model for floor plan restrictions and the barometric change within a particle filter. 
+The state transition samples a new state based on the heading change, altitude change and a fixed step length. 
+The experiments of \cite{Nurminen2014} clearly emphasize the benefits of smoothing techniques. The estimation error could be decreased significantly. 
+However, a fixed-lag smoother was treated only in theory.
+
+In the work of \cite{Paul2009} both fixed-interval and fixed-lag smoothing were presented. 
+They implemented Wi-Fi, binary infra-red motion sensors, binary foot-switches and a potential field for floor plan restrictions. 
+Those sensors were incorporated using a sigma-point Kalman filter in combination with a forward-backward smoother.
+It was also proven by \cite{Paul2009}, that the fixed-lag smoother is slightly less accurate then the fixed-interval smoother. 
+As one would expect from the theoretical foundation.
+Unfortunately, even a sigma-point Kalman filters is after all just a linearisation and therefore not as flexible and suited for the complex problem of indoor localisation as a non-linear estimator like a particle filter. 
+\commentByToni{Kann das jemand nochmal verifizieren? Das mit dem Kalman Filter. Danke.}
+Additionally, the Wi-Fi RSSI model requires known calibration points and is deployed using a remarkable number of access points for very small spaces.  
+In our opinion this is not practical and we would further recommend adding a PDR-based transition instead of a random one. 
+
+In contrast, the here presented approach is able to use two different smoothing algorithm, both implemented as fixed-interval and fixed-lag versions. 
+Further, our localisation system presented in \cite{Ebner-16} enables us to walk stairs and therefore going into the third dimension.
+Therefore, a regularly tessellated graph is utilized to avoid walls, detecting doors and recognizing stairs. 
+Within this work, this is additionally supported by a simple classification that detects the activities  unknown, standing, walking and walking stairs. 
+Finally, we incorporate prior navigation knowledge by using syntactically calculated realistic human walking paths \cite{Ebner-16}. 
+This method makes use of the given destination and thereby provides a more targeted movement. 
+