14 lines
1.3 KiB
TeX
14 lines
1.3 KiB
TeX
\begin{abstract}
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Indoor localisation continues to be a topic of growing importance.
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Despite the advances made, several profound problems are still present.
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For example, estimating an accurate position from a multimodal distribution or recovering from the influence of faulty measurements.
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Within this work, we solve such problems with the help of Monte Carlo smoothing methods, namely forward-backward smoother and backward simulation.
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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.
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This enables many possibilities for further improving the position estimation.
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Both smoothing techniques are deployed as fixed-lag and fixed-interval smoother and a novel approach for incorporating them easily within a conventional localisation system is presented.
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All this is evaluated on four floors within our faculty building.
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The results show that smoothing methods offer a great tool for improving the overall localisation.
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Especially fixed-lag smoothing provides a great runtime support by reducing temporal errors and improving the overall estimation with affordable costs.
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\end{abstract}
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%\begin{IEEEkeywords} indoor positioning, Monte Carlo smoothing, particle smoothing, sequential Monte Carlo\end{IEEEkeywords}
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