diff --git a/code/CMakeLists.txt.user b/code/CMakeLists.txt.user index 47f1584..89c7d5d 100644 --- a/code/CMakeLists.txt.user +++ b/code/CMakeLists.txt.user @@ -1,6 +1,6 @@ - + EnvironmentId diff --git a/tex/bare_conf.dvi b/tex/bare_conf.dvi index 69a89f9..300aedf 100644 Binary files a/tex/bare_conf.dvi and b/tex/bare_conf.dvi differ diff --git a/tex/chapters/abstract.tex b/tex/chapters/abstract.tex index e333d3a..0291b14 100644 --- a/tex/chapters/abstract.tex +++ b/tex/chapters/abstract.tex @@ -1,4 +1,13 @@ - \begin{abstract} - +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}