\section{Conclusion}

In this work we presented an approach for mixing two different localisation schemes using an IMMPF and a non-trivial Markov switching process, which is easy to adapt to many existing systems.
By mixing two particle sets based upon the Kullback-Leibler divergence and a \docWIFI{} quality factor, we were able to satisfy the need of diversity and focus to recover from sample impoverishment in context of indoor localisation. 
It was shown, that the here presented approach is able to improve the robustness, without increasing the error.
However, in some rare situations given bad \docWIFI{} readings we were not able to increase the results. 
This requires further investigations regarding the \docWIFI{} quality factor. 

Finally, the possibility of combining different localisation models enables many new approaches and techniques. 
By incorporating completely different modes, not only transitions, the robustness and accuracy can be further increased.
This would additionally allow for on-the-fly switching between sensor models, e.g. different signal strength prediction methods.
Such a modular solution could be able to fit any environment and thus form a highly flexible and adjustable localisation system. 
However, adjusting the Markov switching process to any number of modes is no easy task and therefore requires intensive future work.


%only the position estimation of the dominant filter. However, would also be possible to combine estimations provided by different modes. 
