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toni
2018-06-27 13:17:54 +02:00
parent c99b276c4f
commit 1e909acb5d
9 changed files with 13875 additions and 5 deletions
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12
tex/chapters/experiments.tex
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@@ -68,8 +68,9 @@ Other transmitters like smart TVs or smartphone hotspots are ignored as they mig
%
The 4 chosen walking paths can be seen in fig. \ref{fig:floorplan}.
They were carried out be 4 different male testers using either a Samsung Note 2, Google Pixel One or Motorola Nexus 6 for recording the measurements.
\todo{walks noch weng erläutern, länge und dauer, many different other walks were made, however those 4 where chosen because most challenging etc. pp.}
All in all, we recorded \SI{28}{} distinct measurement series, \SI{7}{} for each walk.
The picked walks contain erroneous situations, in which many of the above treated problems occur.
Thus we are able to discuss everything in detail.
A walk is indicated by a set of numbered markers, fixed to the ground.
Small icons on those markers give the direction of the next marker and in some cases provide instructions to pause walking for a certain time.
The intervals for pausing vary between \SI{10}{\second} to \SI{60}{\second}.
@@ -80,12 +81,13 @@ Thus, the ground truth might not be \SI{100}{\percent} accurate, but fair enough
The approximation error is then calculated by comparing the interpolated ground truth position with the current estimation \cite{Fetzer-16}.
%computation und monte carlo runs
For each walk we deployed 100 runs using \SI{5000}{particles} particles.
For each walk we deployed 100 runs using \SI{5000}{particles}.
Instead of an initial position and heading, all walks start with a uniform distribution (random position and heading) as prior.
The overall localisation results can be see in table \ref{table:overall}.
Here, we differ between the single impoverishment techniques.
Here, we differ between the respective impoverishment techniques presented in chapter \ref{sec:impo}.
For a better overview, we only used the KDE-based estimation, as the errors compared to the weighted average estimation differ by only a few centimetres.
a simple filter (weighted average estimation + simple impoverishment solution) and an advanced filter (KDE estimation + $D_\text{KL}$-based impoverishment solution).
It can be seen that...
@@ -100,11 +102,11 @@ we want to show a real worst case scenario!
\newcommand{\STAB}[1]{\begin{tabular}{@{}c@{}}#1\end{tabular}}
\begin{table}[!h]
\begin{table}[t]
\centering
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\hline
Method & \multicolumn{3}{c|}{none} & \multicolumn{3}{c|}{simple} & \multicolumn{3}{c|}{kde}\\
Method & \multicolumn{3}{c|}{none} & \multicolumn{3}{c|}{simple} & \multicolumn{3}{c|}{$D_\text{KL}$}\\
\hline
& $\bar{x}$ & $\bar{\sigma}$ & $\tilde{x}_{75}$ & $\bar{x}$ & $\bar{\sigma}$ & $\tilde{x}_{75}$ & $\bar{x}$ & $\bar{\sigma}$ & $\tilde{x}_{75}$ \\
\hline \hline

1
tex/chapters/misc.tex
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@@ -17,6 +17,7 @@ To handle this phenomenon of weight degeneracy, a resampling procedure is perfor
\input{chapters/estimation}
\subsection{Sample Impoverishment}
\label{sec:impo}
As we have extensively discussed in \cite{Fetzer-17}, besides sample degeneracy, particle filters (and nearly all of its modifications) continue to suffer from another notorious problem: sample impoverishment.
It refers to a situation, in which the filter is unable to sample enough particles into proper regions of the building, caused by a high concentration of misplaced particles.
%Such situations are strongly influenced by the resampling step and most of all by restrictive transition models.

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tex/gfx/errorOverTime.eps Normal file
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tex/gfx/errorOverTime.tex Normal file
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tex/gfx/multimodalPath.eps Normal file
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\expandafter\def\csname LT0\endcsname{\color{black}}%
\expandafter\def\csname LT1\endcsname{\color{black}}%
\expandafter\def\csname LT2\endcsname{\color{black}}%
\expandafter\def\csname LT3\endcsname{\color{black}}%
\expandafter\def\csname LT4\endcsname{\color{black}}%
\expandafter\def\csname LT5\endcsname{\color{black}}%
\expandafter\def\csname LT6\endcsname{\color{black}}%
\expandafter\def\csname LT7\endcsname{\color{black}}%
\expandafter\def\csname LT8\endcsname{\color{black}}%
\fi
\fi
\setlength{\unitlength}{0.0500bp}%
\ifx\gptboxheight\undefined%
\newlength{\gptboxheight}%
\newlength{\gptboxwidth}%
\newsavebox{\gptboxtext}%
\fi%
\setlength{\fboxrule}{0.5pt}%
\setlength{\fboxsep}{1pt}%
\begin{picture}(5040.00,2880.00)%
\gplgaddtomacro\gplbacktext{%
}%
\gplgaddtomacro\gplfronttext{%
\csname LTb\endcsname%%
\put(1370,2711){\makebox(0,0)[r]{\strut{}\footnotesize{ground truth}}}%
\csname LTb\endcsname%%
\put(1370,2491){\makebox(0,0)[r]{\strut{}\footnotesize{particles @ 13.4 s}}}%
\csname LTb\endcsname%%
\put(1370,2271){\makebox(0,0)[r]{\strut{}\footnotesize{particles @ 20.8 s}}}%
\csname LTb\endcsname%%
\put(1370,2711){\makebox(0,0)[r]{\strut{}\footnotesize{ground truth}}}%
\csname LTb\endcsname%%
\put(1370,2491){\makebox(0,0)[r]{\strut{}\footnotesize{particles @ 13.4 s}}}%
\csname LTb\endcsname%%
\put(1370,2271){\makebox(0,0)[r]{\strut{}\footnotesize{particles @ 20.8 s}}}%
}%
\gplbacktext
\put(0,0){\includegraphics{walk}}%
\gplfronttext
\end{picture}%
\endgroup