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FrankE
2017-05-08 14:38:14 +02:00
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tex/chapters/experiments.tex
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@@ -2,13 +2,14 @@
% intro
\commentByFrank{reihenfolge so jetzt klar?}
Within our experiments we will first have a look at model optimizations to reduce the error
between model predictions and real-world conditions.
Hereafter we examine the resulting accuracy when using the optimized models for localization
using just the \docWIFI{} component without additional sensors or assumptions.
Within our experiments we will first have a look at model optimizations to reduce the error (in \decibel)
between model predictions and real-world conditions in section \ref{sec:evalModelOpt}.
%
Hereafter, in section \ref{sec:evalWifiMeter} we examine the resulting accuracy (in \meter)
when using the optimized models for localization solely by the \docWIFI{} component without additional sensors, assumptions or filtering.
%
Finally, all models are evaluated in the context of our indoor localization system \refeq{eq:recursiveDensity},
using additional smartphone sensors and the building's floorplan.
using additional smartphone sensors and the building's floorplan in section \ref{sec:evalFiltered}.
All optimizations and evaluations took place within two adjacent buildings (4 and 2 floors, respectively)
and two connected outdoor regions (entrance and inner courtyard),
@@ -35,9 +36,10 @@
% -------------------------------- optimization -------------------------------- %
\subsection{Model optimization}
\label{sec:evalModelOpt}
As the signal strength prediction model is the core of the absolute positioning component
described in section \ref{sec:system}, we start with the model parameter optimization (see \ref{sec:optimization}).
described in section \ref{sec:system}, we start with the model parameter optimization (see section \ref{sec:optimization}).
\mTXP{}, \mPLE{} and \mWAF{} will be estimated based on some reference measurements using
various optimization strategies. The results of those optimization strategies are compared
with each other and an empiric parameter choice:
@@ -48,7 +50,7 @@
\reffig{fig:referenceMeasurements} depicts the location of the used 121 reference measurements.
Each location was scanned 30 times ($\approx$ \SI{25}{\second} scan time),
non-permanent \docAP{}s were removed, the values were grouped per physical transmitter (see \ref{sec:vap})
non-permanent \docAP{}s were removed, the values were grouped per physical transmitter (see section \ref{sec:vap})
and aggregated to form the average signal strength per transmitter.
\begin{figure}
@@ -64,8 +66,8 @@
\begin{subfigure}[t!]{0.48\textwidth}
\input{gfx2/model-bboxes.tex}
\caption{
Each distinct floor-color denotes one model (7 in total) for {\em \optPerRegion{}}.
Often more than one bounding box is needed to approximate the region's shape.
Each distinct floor-color denotes a region (6 indoors, 1 outdoors) for {\em \optPerRegion{}}.
Often more than one bounding box is needed to describe the region's shape.
}
\label{fig:modelBBoxes}
\end{subfigure}
@@ -120,17 +122,17 @@
\item{
{\em\optParamsAllAP{}} is the same as above, except that the three parameters are optimized
using the reference measurements. However, all transmitters share the same three parameters.
using the reference measurements (convex function). All transmitters share the same three parameters.
}
\item{
{\em\optParamsEachAP{}} optimizes the three parameters per \docAP{} instead of using the same
parameters for all.
parameters for all. This still denotes a convex function per transmitter.
}
\item{
{\em\optParamsPosEachAP{}} does not need any prior knowledge and will optimize all six parameters
(3D position, \mTXP, \mPLE, \mWAF) based on the reference measurements.
(3D position, \mTXP, \mPLE, \mWAF) based on the reference measurements (non-convex function).
}
\item{
@@ -345,11 +347,11 @@
% -------------------------------- wifi walk error -------------------------------- %
\subsection{\docWIFI{} location estimation error}
\label{sec:evalWifiMeter}
\todo{uebergang jetzt besser?}
Having optimized several signal strength prediction models, we can now examine the resulting localization
accuracy for each. For now, this will just cover the \docWIFI{} component itself.
The impact of adding additional sensors and a transition model will be evaluated later.
accuracy (in \meter) for each. For now, this will just cover the \docWIFI{} component itself.
The impact of fusing additional sensors and a adding prior knowledge provided by a transition model will be evaluated later.
%Using the optimized model setups and the measurements $\mRssiVec$ determined by scanning for nearby \docAPshort{}s,
@@ -381,7 +383,8 @@
In \refeq{eq:bestWiFiPos} $\mu_{i,\mPosVec}$ is the signal strength for \docAP{} $i$,
installed at location $\mPosVec$, returned from the to-be-examined prediction model.
For all comparisons, we use a constant uncertainty of $\sigma = \SI{8}{\decibel}$.
For all comparisons, we use a constant uncertainty of $\sigma = \SI{8}{\decibel}$,
which is an empirical choice based on prior experiments.
The quality of the estimated location is determined by using the Euclidean distance between estimation
$\mPosVec^*$ and the pedestrian's ground truth position at the time the scan $\mRssiVec$
@@ -403,6 +406,9 @@
\caption{
Overview of all conducted paths, each starting at the denoted rectangle.
Outdoor areas are marked in green.
The length of the paths is as follows:
path 1: \SI{207}{\meter}, path 2: \SI{138}{\meter}, path 3: \SI{86}{\meter}, path 4: \SI{140}{\meter},
and path 5: \SI{97}{\meter}.
}
\label{fig:allWalks}
\end{figure}
@@ -491,10 +497,10 @@
as likely as the pedestrian's actual location, we examined various approaches.
Unfortunately, most of which did not provide a viable enhancement under all conditions for the performed walks.
\commentByFrank{ja, eig gehoert das vor in die theorie, aber da es so kurz ist und vorne immer die ueberleitung kaputt macht
oder anderen dingen vorgreifen wuerde, steht es hier}
%\commentByFrank{ja, eig gehoert das vor in die theorie, aber da es so kurz ist und vorne immer die ueberleitung kaputt macht
%oder anderen dingen vorgreifen wuerde, steht es hier}
The misclassification-rate is determined by counting the amount of (random) locations within
the building that produce a similar probability \refeq{eq:wifiProb} as the actual ground-truth
the building that produce a similar probability \refeq{eq:wifiProb} compared to the actual ground-truth
position.
One possibility to dissolve such an equal \docWIFI{}-likelihood between two (or more) locations is,
@@ -574,6 +580,7 @@
% -------------------------------- final system -------------------------------- %
\subsection{Filtered location estimation error}
\label{sec:evalFiltered}
After examining the \docWIFI{} component on its own, we will now analyze the impact of previously discussed model
optimizations on our smartphone-based indoor localization system described in section \ref{sec:system}, based on