\section{Conclusion}
We presented a novel approach to integrate prior navigation knowledge by using realistic human walking paths. 
Based on a weighted graph, two different models for walking in a targeted and natural manner were introduced. 
It could be shown that adding this additional knowledge causes an overall improvement of the localisation results, while maintaining flexibility for unexpected behaviour. 
Furthermore, our approach is able to provide accurate and robust position estimations, even when (usually) necessary calibration processes are avoided.

However, providing this calibration knowledge can further improve the results. 
In order to reduce the effort of locating and calibrating \docAP{}s, a numerical optimization based on 
measurements during predefined walks could be considered. 
Additionally, the graph allows for storing pre-computed signal strengths and thus enables more complex
prediction models e.g. incorporating wall information.

As seen, multimodal distributions lead to faulty position estimations and therefore rising errors. 
One possible method to resolve this issue would be a more suiting location estimation technique. 
Another promising way is smoothing. 
By deploying a fixed-lag smoother the system would still be perceived as real-time application, but is able to calculate the (delayed) estimation using future measurements up to the latest timestep.



%\commentByFrank{balance zwischen den einzelnen wahrscheinlichkeiten ist oft ein schmaler grad. wieviel turn erlauben, wieviel auf den pfad zwingen. das verbesern}
%\commentByFrank{position der APs wissen ist viel arbeit. vereinfachen durch test-walks auf vorgegebenen pfaden -> numerisch optimieren wo APs sind}
%\commentByToni{quadtress. stellen die groesse der zellen variable ein. je nach bedarf.}
%\commentByFrank{multimodalitaeten (z.B. treppenhaeuser). fixen durch andere estimations}
%\commentByToni{oder durch smoothing}
%\commentByToni{Aufzuege hinzufuegen. Vertical Acceleration benutzen.}
	
