fixed graphics in experiments using subfig package
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@@ -50,9 +50,10 @@ FILE(GLOB SOURCES
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main.cpp
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mainTrilat.cpp
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mainProb.cpp
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mainPlotting.cpp
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Eval.cpp
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FtmKalman.cpp
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trilateration.cpp
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trilateration.cpp
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)
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@@ -16,7 +16,7 @@ namespace Settings {
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const bool PlotToPng = false;
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const bool UseKalman = false;
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const bool UseRSSI = false;
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const bool UseRSSI = true;
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/** describes one dataset (map, training, parameter-estimation, ...) */
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@@ -333,7 +333,6 @@ static CombinedStats<float> run(Settings::DataSetup setup, int walkIdx, std::str
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{
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plot.addCircle(10000 + nucConfig.second.ID, nucConfig.second.position.xy(), 0.1);
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}
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plot.plot();
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// particle-filter
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const int numParticles = 5000;
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@@ -716,6 +715,11 @@ int main(int argc, char** argv)
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std::cout << "Trilateration" << "\n";
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return mainTrilat(argc, argv);
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}
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else if (args.hasFlag("plot"))
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{
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std::cout << "Plotting" << "\n";
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return mainPlotting(argc, argv);
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}
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CombinedStats<float> statsAVG;
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CombinedStats<float> statsMedian;
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@@ -3,3 +3,5 @@
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int mainTrilat(int argc, char** argv);
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int mainProp(int argc, char** argv);
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int mainPlotting(int argc, char** argv);
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@@ -16,7 +16,6 @@
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\usepackage{color, colortbl}
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\usepackage{graphicx}
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\usepackage{subcaption}
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\interdisplaylinepenalty=2500
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@@ -29,6 +28,8 @@
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\usepackage{array}
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\usepackage{multirow}
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\usepackage{xfrac}
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\usepackage{mwe}
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\usepackage{subfig}
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%\updates{yes} % If there is an update available, un-comment this line
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@@ -19,4 +19,11 @@
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\etal{Ibrahim} lays the required groundwork to use the still experimental FTM standard and verifies the general accuracy \cite{ibrahim2018verification}.
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\etal{Yu} present a system using FTM measurements and multisensor multi-pattern-based dead reckoning based on a Unscented Kalman filter sensor fusion \cite{yu2019robust}.
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\etal{Xu} \cite{xu2019locating}.
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\etal{Xu} \cite{xu2019locating}.
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Compared to the above state of the art our work...
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\begin{itemize}
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\item szenario realistischer und nicht nur quadrate die man läuft
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\item direkter vergleich mit RSSI
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\item in der praxis erprobte verfahren darauf laufen lassen.
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\end{itemize}
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@@ -97,34 +97,23 @@ The geometry and properties of the internal antenna are unknown.
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% Erkenntnis 4: Unterschied zwischen Pixel 2 und 3 ist nicht erkennbar
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\begin{figure}[ht]
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\begin{minipage}[t]{0.48\textwidth}
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\centering
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\includegraphics[width=1\textwidth]{DistMeasMean.png}
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\caption{Mean distance per WiFi card}
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\end{minipage}
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\begin{minipage}[t]{0.48\textwidth}
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\centering
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\includegraphics[width=1\textwidth]{DistMeasMeanPerPixel.png}
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\caption{Mean distance per Pixel}
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\end{minipage}
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\begin{minipage}[t]{1.0\textwidth}
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\centering
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\includegraphics[width=1\textwidth]{DistMeasCDF.png}
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\caption{Dist. Meas CDF}
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\end{minipage}
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\begin{minipage}{.5\textwidth}
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\centering
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\subfloat[]{\label{main:a}\includegraphics[width=\textwidth]{DistMeasMean.png}}
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\end{minipage}%
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\begin{minipage}{.5\textwidth}
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\centering
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\subfloat[]{\label{main:b}\includegraphics[width=\textwidth]{DistMeasMeanPerPixel.png}}
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\end{minipage}\par\medskip
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\centering
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\subfloat[CDF of error]{\label{main:c}\includegraphics[width=\textwidth]{DistMeasCDF.png}}
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\caption{my fig}
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\label{fig:main}
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\end{figure}
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\subsection{Localization performance}
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\begin{itemize}
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\item Location error per method (multilateration, prob, particle filter)
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\item Wie gut geht der PF? Parameter und Szenarien
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\item RSSI vs FTM; wo ist FTM besser wo schlechter?; Verhält es sich ähnlich?
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\end{itemize}
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\subsection{Results for Multilateration}
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zunächst wird das einfachste und nahliegendste verfahren untersucht um die performance von ftm und rssi gegenüberzustellen.
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