14 lines
1.3 KiB
TeX
14 lines
1.3 KiB
TeX
\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.
|