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switched to MDPI journal layout

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FrankE
2017-06-29 15:37:38 +02:00
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34
tex/chapters/abstract.tex
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@@ -1,5 +1,5 @@
\begin{abstract}
\abstract{%
%
Indoor localization and indoor pedestrian navigation is an active field of research
with increasing attention.
%
@@ -12,7 +12,7 @@
While more complex models provide an increased accuracy by including architectural knowledge
about walls and other obstacles, they often require additional computation during runtime and
demand for prior knowledge during setup.
\\%
Within this work we will thus focus on simple, easy to set-up models and evaluate their
performance compared to real-world measurements. The evaluation ranges from a fully empiric, instant
setup, given the transmitter locations are well-known, to a highly-optimized scenario based
@@ -23,36 +23,18 @@
All of the optimized models are evaluated within an actual smartphone-based
indoor localization system. This system uses the phone's \docWIFI{}, barometer and IMU
to infer the pedestrian's current location via recursive density estimation based on particle filtering.
\\%
We will show that while a \SI{100}{\percent} empiric parameter choice for the model already provides enough
accuracy for many use-cases, a small number of reference measurements is enough to dramatically increase
such a system's performance.
%
}
%system setup kostet oft sehr viel zeit [fingerprinting kostet]
%deshalb werden alternativen untersucht:
%bekannte AP position mit empirischen parametern
%und optimierung durch einige referenzmessungen
%floorplan wird für die navigation bzw orientierung des anwenders eh gebraucht
%dann kann man ihn auch gleich für ein bewegungsmodell nutzen
%es sollte klar werden, dass es auch darum geht, effizient
%auf einem normalen smartphone lauffähig zu sein [passend zum journal]
\end{abstract}
% TODO
\begin{CCSXML}
\end{CCSXML}
%\ccsdesc[500]{Computer systems organization~Embedded systems}
%\ccsdesc[300]{Computer systems organization~Redundancy}
%\ccsdesc{Computer systems organization~Robotics}
%s\ccsdesc[100]{Networks~Network reliability}
\keywords{\docWIFI{}, indoor localization, sensor fusion}

3
tex/chapters/conclusion.tex
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@@ -36,8 +36,7 @@
While we were able to improve the performance of the \docWIFI{} sensor component,
the filtering process should be more robust against erroneous observations.
Getting stuck should be prevented, independent of minor changes in quality for
the signal strength prediction model \cite{todo-toni}.
\commentByFrank{cite auf toni?!}
the signal strength prediction model \cite{Fetzer-17}.
Our \docWIFI{} quality metric often was able to determine situations that
would yield multimodal or bad \docWIFI{} estimations and temporarily

76
tex/chapters/experiments.tex
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@@ -18,8 +18,12 @@
Within all \docWIFI{} observations, we only consider the \docAP{}s that are permanently installed
and can be identified by their well-known MAC address.
Temporal and movable transmitters like smart TVs or smartphone hotspots are ignored as they might cause estimation errors.
The \docWIFI{} quality factor from section \ref{sec:wifiQuality} was configured with
a lower bound of \SI{-85}{\decibel m}, an upper bound of \SI{-70}{\decibel m} and a threshold of $0.25$.
%
Unfortunately, due to non-disclosure agreements, we are not allowed to depict the actual location
Unfortunately, due to legal reasons, our institution does not allow depicting the actual location
of installed transmitters within the following figures.
@@ -157,26 +161,27 @@
distance between estimated and real position is $\sim$\SI{8}{\meter} and the maximum $\sim$\SI{27}{\meter}.
For \SI{68}{\percent} of all installed transmitters, the estimated floor-number matched the real location.
\newcommand{\tablefont}{\scriptsize}
\begin{figure}
% cumulative error density
\centering
\begin{subfigure}{0.52\textwidth}
\input{gfx2/wifi_model_error_0_95.tex}
\end{subfigure}
% table
\end{subfigure}%
\begin{subfigure}{0.47\textwidth}
\smaller
\centering
\begin{tabular}{|l|c|c|c|c|}
\hline
& 25 \% & median & 75 \% & avg \\\hline
\noOptEmpiric{} & \SI{2.5}{\decibel} & \SI{5.6}{\decibel} & \SI{9.3}{\decibel} & \SI{6.5}{\decibel} \\\hline
\optParamsAllAP{} & \SI{2.0}{\decibel} & \SI{4.3}{\decibel} & \SI{7.5}{\decibel} & \SI{5.4}{\decibel} \\\hline
\optParamsEachAP{} & \SI{1.6}{\decibel} & \SI{3.3}{\decibel} & \SI{6.2}{\decibel} & \SI{4.4}{\decibel} \\\hline
\optParamsPosEachAP{} & \SI{1.5}{\decibel} & \SI{3.0}{\decibel} & \SI{5.5}{\decibel} & \SI{3.8}{\decibel} \\\hline
\optPerFloor{} & \SI{0.7}{\decibel} & \SI{1.6}{\decibel} & \SI{3.3}{\decibel} & \SI{2.6}{\decibel} \\\hline
\optPerRegion{} & \SI{0.6}{\decibel} & \SI{1.4}{\decibel} & \SI{3.1}{\decibel} & \SI{2.4}{\decibel} \\\hline
\end{tabular}
\vspace{9mm}
\begin{tabular}{|l|c|c|c|c|}
\hline
& \tablefont 25 \% & \tablefont median & \tablefont 75 \% & \tablefont avg \\\hline
\tablefont\noOptEmpiric{} & \tablefont\SI{2.5}{\decibel} & \tablefont\SI{5.6}{\decibel} & \tablefont\SI{9.3}{\decibel} & \tablefont\SI{6.5}{\decibel} \\\hline
\tablefont\optParamsAllAP{} & \tablefont\SI{2.0}{\decibel} & \tablefont\SI{4.3}{\decibel} & \tablefont\SI{7.5}{\decibel} & \tablefont\SI{5.4}{\decibel} \\\hline
\tablefont\optParamsEachAP{} & \tablefont\SI{1.6}{\decibel} & \tablefont\SI{3.3}{\decibel} & \tablefont\SI{6.2}{\decibel} & \tablefont\SI{4.4}{\decibel} \\\hline
\tablefont\optParamsPosEachAP{} & \tablefont\SI{1.5}{\decibel} & \tablefont\SI{3.0}{\decibel} & \tablefont\SI{5.5}{\decibel} & \tablefont\SI{3.8}{\decibel} \\\hline
\tablefont\optPerFloor{} & \tablefont\SI{0.7}{\decibel} & \tablefont\SI{1.6}{\decibel} & \tablefont\SI{3.3}{\decibel} & \tablefont\SI{2.6}{\decibel} \\\hline
\tablefont\optPerRegion{} & \tablefont\SI{0.6}{\decibel} & \tablefont\SI{1.4}{\decibel} & \tablefont\SI{3.1}{\decibel} & \tablefont\SI{2.4}{\decibel} \\\hline
\end{tabular}
\vspace{8mm}
\end{subfigure}
\caption{
Cumulative error distribution for all optimization strategies. The error results from the (absolute) difference
@@ -189,6 +194,7 @@
\begin{figure}
\centering
\begin{subfigure}{0.32\textwidth}
\centering
\input{gfx2/wifiMaxErrorNN_opt0.tex}
@@ -261,6 +267,7 @@
region for the optimization to converge.
\begin{figure}
\centering
\begin{subfigure}{0.49\textwidth}
\input{gfx2/wifi_model_error_num_fingerprints_method_5_0_90.tex}
\end{subfigure}
@@ -414,6 +421,7 @@
\end{figure}
\begin{figure}
\centering
% error gfx
\begin{subfigure}{0.52\textwidth}
\centering
@@ -427,19 +435,19 @@
%4.30191 6.91534 14.0746 11.948
%4.26189 6.35975 11.5646 10.7466
\begin{subfigure}{0.47\textwidth}
\smaller
\footnotesize
\centering
\begin{tabular}{|l|c|c|c|c|}
\hline
& \SI{25}{\percent} & median & \SI{75}{\percent} & avg \\\hline
\noOptEmpiric{} & \SI{6.0}{\meter} & \SI{9.2}{\meter} & \SI{14.4}{\meter} & \SI{11.9}{\meter} \\\hline
\optParamsAllAP{} & \SI{6.5}{\meter} & \SI{9.0}{\meter} & \SI{12.8}{\meter} & \SI{12.0}{\meter} \\\hline
\optParamsEachAP{} & \SI{6.8}{\meter} & \SI{9.8}{\meter} & \SI{13.8}{\meter} & \SI{13.0}{\meter} \\\hline
\optParamsPosEachAP{} & \SI{5.4}{\meter} & \SI{8.6}{\meter} & \SI{14.8}{\meter} & \SI{12.0}{\meter} \\\hline
\optPerFloor{} & \SI{4.3}{\meter} & \SI{6.9}{\meter} & \SI{14.0}{\meter} & \SI{11.9}{\meter} \\\hline
\optPerRegion{} & \SI{4.2}{\meter} & \SI{6.5}{\meter} & \SI{11.6}{\meter} & \SI{10.7}{\meter} \\\hline
& \tablefont\SI{25}{\percent} & \tablefont median & \tablefont\SI{75}{\percent} & \tablefont avg \\\hline
\tablefont\noOptEmpiric{} & \tablefont\SI{6.0}{\meter} & \tablefont\SI{9.2}{\meter} & \tablefont\SI{14.4}{\meter} & \tablefont\SI{11.9}{\meter} \\\hline
\tablefont\optParamsAllAP{} & \tablefont\SI{6.5}{\meter} & \tablefont\SI{9.0}{\meter} & \tablefont\SI{12.8}{\meter} & \tablefont\SI{12.0}{\meter} \\\hline
\tablefont\optParamsEachAP{} & \tablefont\SI{6.8}{\meter} & \tablefont\SI{9.8}{\meter} & \tablefont\SI{13.8}{\meter} & \tablefont\SI{13.0}{\meter} \\\hline
\tablefont\optParamsPosEachAP{} & \tablefont\SI{5.4}{\meter} & \tablefont\SI{8.6}{\meter} & \tablefont\SI{14.8}{\meter} & \tablefont\SI{12.0}{\meter} \\\hline
\tablefont\optPerFloor{} & \tablefont\SI{4.3}{\meter} & \tablefont\SI{6.9}{\meter} & \tablefont\SI{14.0}{\meter} & \tablefont\SI{11.9}{\meter} \\\hline
\tablefont\optPerRegion{} & \tablefont\SI{4.2}{\meter} & \tablefont\SI{6.5}{\meter} & \tablefont\SI{11.6}{\meter} & \tablefont\SI{10.7}{\meter} \\\hline
\end{tabular}
\vspace{9mm}
\vspace{7mm}
\end{subfigure}
\caption {
Cumulative error distribution between walked ground truth and \docWIFI{}-only location estimation using \refeq{eq:bestWiFiPos}.
@@ -470,6 +478,7 @@
% -------------------------------- plots indicating walk issues -------------------------------- %
\begin{figure}
\centering
\input{gfx2/wifiMultimodality.tex}
\caption{
\docWIFI{}-only location probability for three distinct scans where
@@ -652,6 +661,7 @@
keep the pedestrian's heading until the signal quality reached sane levels again.
\begin{figure}
\centering
\begin{subfigure}{0.49\textwidth}
\input{gfx2/overall-system-error.tex}
\end{subfigure}
@@ -669,16 +679,16 @@
\begin{tabular}{|l|c|c|c|c|c|}
\hline
& \SI{25}{\percent} & median & \SI{75}{\percent} & avg & stuck \\\hline
\noOptEmpiric{} & \SI{2.6}{\meter} & \SI{5.1}{\meter} & \SI{11.2}{\meter} & \SI{9.0}{\meter} & \SI{22}{\percent} \\\hline
\optParamsAllAP{} & \SI{2.9}{\meter} & \SI{6.0}{\meter} & \SI{12.4}{\meter} & \SI{10.7}{\meter} & \SI{15}{\percent} \\\hline
\optParamsEachAP{} & \SI{1.9}{\meter} & \SI{4.0}{\meter} & \SI{7.9}{\meter} & \SI{5.8}{\meter} & \SI{5}{\percent} \\\hline
\optParamsPosEachAP{} & \SI{1.9}{\meter} & \SI{3.9}{\meter} & \SI{7.1}{\meter} & \SI{5.6}{\meter} & \SI{5}{\percent} \\\hline
\optPerFloor{} & \SI{1.6}{\meter} & \SI{3.2}{\meter} & \SI{6.1}{\meter} & \SI{4.8}{\meter} & \SI{4}{\percent} \\\hline
\optPerRegion{} & \SI{1.6}{\meter} & \SI{3.3}{\meter} & \SI{6.5}{\meter} & \SI{5.0}{\meter} & \SI{4}{\percent} \\\hline
& \tablefont\SI{25}{\percent} & \tablefont median & \tablefont\SI{75}{\percent} & \tablefont avg & \tablefont stuck \\\hline
\tablefont\noOptEmpiric{} & \tablefont\SI{2.6}{\meter} & \tablefont\SI{5.1}{\meter} & \tablefont\SI{11.2}{\meter} & \tablefont\SI{9.0}{\meter} & \tablefont\SI{22}{\percent} \\\hline
\tablefont\optParamsAllAP{} & \tablefont\SI{2.9}{\meter} & \tablefont\SI{6.0}{\meter} & \tablefont\SI{12.4}{\meter} & \tablefont\SI{10.7}{\meter} & \tablefont\SI{15}{\percent} \\\hline
\tablefont\optParamsEachAP{} & \tablefont\SI{1.9}{\meter} & \tablefont\SI{4.0}{\meter} & \tablefont\SI{7.9}{\meter} & \tablefont\SI{5.8}{\meter} & \tablefont\SI{5}{\percent} \\\hline
\tablefont\optParamsPosEachAP{} & \tablefont\SI{1.9}{\meter} & \tablefont\SI{3.9}{\meter} & \tablefont\SI{7.1}{\meter} & \tablefont\SI{5.6}{\meter} & \tablefont\SI{5}{\percent} \\\hline
\tablefont\optPerFloor{} & \tablefont\SI{1.6}{\meter} & \tablefont\SI{3.2}{\meter} & \tablefont\SI{6.1}{\meter} & \tablefont\SI{4.8}{\meter} & \tablefont\SI{4}{\percent} \\\hline
\tablefont\optPerRegion{} & \tablefont\SI{1.6}{\meter} & \tablefont\SI{3.3}{\meter} & \tablefont\SI{6.5}{\meter} & \tablefont\SI{5.0}{\meter} & \tablefont\SI{4}{\percent} \\\hline
\end{tabular}
\setlength{\tabcolsep}{1.0em} % reset the horizontal padding
\vspace{11.5mm}
\vspace{9.0mm}
\end{subfigure}
%
\caption{

7
tex/chapters/work.tex
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@@ -41,7 +41,7 @@
\label{sec:sigStrengthModel}
\begin{equation}
\mRssi = \mTXP{} + 10 \mPLE{} + \log_{10} \frac{d}{d_0} + \mGaussNoise{}
\mRssi = \mTXP{} - 10 \mPLE{} + \log_{10} \frac{d}{d_0} + \mGaussNoise{}
\label{eq:logDistModel}
\end{equation}
@@ -79,7 +79,7 @@
without costly intersection checks and thus allows for real-time use-cases running on smartphones.
\begin{equation}
\mRssi = \mTXP{} + 10 \mPLE{} + \log_{10} \frac{d}{d_0} + \numFloors{} \mWAF{} + \mGaussNoise{}
\mRssi = \mTXP{} - 10 \mPLE{} + \log_{10} \frac{d}{d_0} + \numFloors{} \mWAF{} + \mGaussNoise{}
\label{eq:logNormShadowModel}
\end{equation}
@@ -284,6 +284,9 @@
In \refeq{eq:wifiQuality} we use the average signal strength $\bar\mRssi$ among all \docAP{}s seen within one measurement
$\mRssiVec$ and scale this value to match a region of $[0, 1]$ depending on an upper and lower bound.
If the returned quality is below a certain threshold, \docWIFI{} is ignored within the evaluation.
Lower and upper bound are chosen empirically by looking at the usual range of \docWIFI{} signal strengths,
that still provide persistent data-connections to clients. The threshold is also determined empirically by examining
the results of \refeq{eq:wifiQuality} for some places with good and bad \docWIFI{} location estimations, respectively.
\begin{equation}
\newcommand{\leMin}{l_\text{min}}