\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 Wi-Fi 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, while keeping the error low.
However, in some rare situations given bad Wi-Fi readings we were not able to increase the results as usual. 
This requires further investigations regarding the Wi-Fi 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 an on-the-fly switching between sensor models, e.g. different signal strength 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.