\begin{abstract}
Indoor localisation continues to be a topic of growing importance. 
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 solve such problems with the 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 a novel approach for incorporating them easily within a conventional localisation system is 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 overall localisation. 
Especially fixed-lag smoothing provides a great runtime support by reducing temporal 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}
