FHWS/IPIN2017
Archived
3
0
Fork 0
Code Issues 4 Pull Requests Releases Wiki Activity

tex v2 - without experiments

Browse Source
This commit is contained in:
toni
2017-05-10 23:33:01 +02:00
parent 5b2e1b0c65
commit 210fae56b7
11 changed files with 4856 additions and 10840 deletions
Download Patch File Download Diff File Expand all files Collapse all files

27
tex/chapters/conclusion.tex
View File

@@ -1,20 +1,13 @@
\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.
In this work we presented .. which is easy to adapt to many existing localisation systems.
combining different filter shemes using an IMMPF
enables us to combine beliebie transition models.
Looking at the results / experiments
we were able to satisfy the need of diversity and focues to reduce the recover from sample impoverishment in context of indoor localization.
The benefits of our approach demonstrated
This further
future work completely different localisation approaches, not only transitions.
more then 2 filters
a more advanced wi-fi quality factor
incorporating a smoothing filter as mode, so we are able to draw new particles from a smoothed particle set. ..
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.