13 lines
1.1 KiB
TeX
13 lines
1.1 KiB
TeX
\section{Conclusion}
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Within this work a novel approach for utilizing the forward-backward smoother and backward simulation to problems of indoor localisation was presented.
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Both were implemented as fixed-lag and fixed-interval smoother.
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It was shown that smoothing methods are able to decrease the estimation error and improving the overall localisation.
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Especially fixed-lag smoothing is a great tool for runtime support by reducing timely errors and improving the overall estimation with affordable costs.
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However, a fixed-lag smoother is not able to change the lag dynamically, as its name suggests.
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Therefore, a dynamic-lag smoother could be able to further improve the estimation by considering higher lags in critical areas.
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Finally, the smoothing transition does not use any information provided by the underlying graph structure.
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This would allow to use environmental informations and to replace the current line-of-sight model with a graph-based one.
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By incorporating the Wi-Fi's signal strength measurements a more advanced smoothing transition should be able to compensate for faulty Wi-Fi measurements and the hereby resulting jumps between positions.
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