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final version of paper

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tex/chapters/conclusion.tex
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\section{Conclusion}
Within this work a novel approach for utilizing \commentByFrank{utiliSing?} the forward-backward smoother and backward simulation to problems of indoor localisation was presented.
Within this work a novel approach for utilising the forward-backward smoother and backward simulation to problems of indoor localisation was presented.
Both were implemented as fixed-lag and fixed-interval smoother.
It was shown that smoothing methods are able to decrease the estimation error and improving \commentByFrank{are able to decrease ... and improve. ING weg?} the overall localisation.
Especially fixed-lag smoothing is a great tool for runtime support by reducing timely \commentByFrank{wieder} errors and improving the overall estimation with affordable costs.
It was shown that smoothing methods are able to decrease the estimation error and improve the overall localisation.
Especially fixed-lag smoothing is a great tool for runtime support by reducing temporal errors and improving the overall estimation with affordable costs.
However, a fixed-lag smoother is not able to change the lag dynamically, as its name suggests.
Therefore, a dynamic-lag smoother could be able to further improve the estimation by considering higher lags in critical areas.