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tex/chapters/abstract.tex

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 \begin{abstract}
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+Indoor localisation continuous to be a topic of growing importance. Many different approaches for estimating the position of a pedestrian are presented year after year. 
+Despite the advances made, several profound problems are still present. 
+For example, estimating an accurate position from a multimodal distribution or recovering from the influence of faulty measurements.
+Within this work, we try do solve such problems with help of Monte Carlo smoothing methods, namely forward-backward smoother and backward simulation.
+In contrast to normal filtering procedures like particle filtering, smoothing methods are able to incorporate future measurements instead of just using current and past data.
+This enables many possibilities for further improving the position estimation. 
+Both smoothing techniques are deployed as fixed-lag and fixed-interval smoother and two novel approaches for incorporating them easily within our localisation system are presented.
+All this is evaluated on four floors within our faculty building.
+The results show that smoothing methods offer a great tool for improving the localisation results. 
+Especially fixed-lag smoothing provides a great runtime support by reducing timely errors and improving the overall estimation with affordable costs.
 \end{abstract}
+%\begin{IEEEkeywords} indoor positioning, Monte Carlo smoothing, particle smoothing, sequential Monte Carlo\end{IEEEkeywords}