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minor code changes for GFX
This commit is contained in:
FrankE
2017-05-02 17:01:21 +02:00
parent 8596732f43
commit 47def9ad90
7 changed files with 281 additions and 95 deletions
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132
tex/chapters/experiments.tex
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@@ -4,12 +4,12 @@
All optimizations and evaluations took place within two adjacent buildings (4 and 2 floors, respectively)
and two connected outdoor regions (entrance and inner courtyard),
yielding a total size of \SI{110}{\meter} x \SI{60}{\meter}.
\SI{110}{\meter} x \SI{60}{\meter} in size.
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 are ignored as they might cause estimation errors.
Temporal and movable transmitters like smart TVs or smartphone hotspots are ignored as they might cause estimation errors.
%
Unfortunately, due to non-disclosure agreements, we are not allowed to depict the actual location
of installed transmitters within the following figures.
@@ -28,37 +28,58 @@
\subsection{Model optimization}
As the signal strength prediction model is the heart of the absolute positioning component
described in \ref{sec:system} we start with the model parameter estimation (see \ref{sec:optimization}) for
\mTXP, \mPLE and \mWAF based on some reference measurements and compare the results
between various optimization strategies and a basic empiric choice of \mTXP = \SI{-40}{\decibel{}m} @ \SI{1}{\meter}
(defined by the usual \docAPshort{} transmit power for europe), a path loss exponent $\mPLE \approx $ \SI{2.5} and
$\mWAF \approx$ \SI{-8}{\decibel} per floor/ceiling (made of reinforced concrete) \todo{cite für werte}.
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 estimation (see \ref{sec:optimization}) for
\mTXP{}, \mPLE{} and \mWAF{} based on some reference measurements and compare the results
between various optimization strategies and a basic empiric choice of \mTXP{} = \SI{-40}{\decibel{}m} @ \SI{1}{\meter}
(defined by the usual \docAPshort{} transmit power for europe), a path loss exponent $\mPLE{} \approx $ \SI{2.5} and
$\mWAF{} \approx$ \SI{-8}{\decibel} per floor / ceiling (made of reinforced concrete) \todo{cite für werte}.
Figure \ref{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})
and aggregated to form the average signal strength per transmitter.
% used reference measurements
\begin{figure}
{
\centering
\input{gfx/all_fingerprints.tex}
}
\caption{
Locations of the 121 reference measurements.
The size of each square denotes the number of permanently installed \docAPshort{}s
that are visible at this location,
and ranges between 2 and 22 with an average of 9.
}
\label{fig:referenceMeasurements}
\begin{subfigure}[t!]{0.48\textwidth}
\input{gfx2/all_fingerprints.tex}
\caption{
The size of each square denotes the number of permanently installed \docAPshort{}s
that were visible while scanning, and ranges between 2 and 22 with an average of 9.
}
\label{fig:referenceMeasurements}
\end{subfigure}
\enskip\enskip
\begin{subfigure}[t!]{0.48\textwidth}
\input{gfx2/model-bboxes.tex}
\caption{
More than one bounding box is needed for each model to approximate the building's shape.
Each distinct floor-color denotes a single model (7 in total).
}
\label{fig:modelBBoxes}
\end{subfigure}
\caption{Locations of the 121 reference measurements (left) and bounding-boxes used for {\em \optPerRegion{}} (right).}
\end{figure}
% used reference measurements
%\begin{figure}
% {
% \centering
% \input{gfx/all_fingerprints.tex}
% }
% \caption{
% Locations of the 121 reference measurements.
% The size of each square denotes the number of permanently installed \docAPshort{}s
% that are visible at this location,
% and ranges between 2 and 22 with an average of 9.
% }
% \label{fig:referenceMeasurements}
%\end{figure}
% visible APs:
% cnt(121) min(2.000000) max(22.000000) range(20.000000) med(8.000000) avg(9.322314) stdDev(4.386709)
\begin{figure}[b]
\begin{figure}
\centering
\input{gfx/compare-wifi-in-out.tex}
\caption{
@@ -83,7 +104,7 @@
with its position, which is well known from the floorplan.
{\em\optParamsAllAP{}} is the same as above, except that the three parameters are optimized
using the reference measurements.
using the reference measurements. However, all transmitters share the same three parameters.
{\em\optParamsEachAP{}} optimizes the three parameters per \docAP{} instead of using the same
parameters for all.
@@ -93,8 +114,7 @@
{\em\optPerFloor{}} and {\em\optPerRegion{}} are just like \optParamsPosEachAP{} except that
there are several sub-models that are optimized for one floor / region instead of the whole building.
\todo{grafik, die die regionen zeigt???}
The chosen bounding boxes and resulting sub-models are depicted in figure \ref{fig:modelBBoxes}.
Figure \ref{fig:wifiModelError} shows the optimization results for all strategies, which are as expected:
The estimation error is indirectly proportional to the number of optimized parameters.
@@ -108,15 +128,40 @@
For \SI{68}{\percent} of all installed transmitters, the estimated floor-number matched the real location.
\begin{figure}
\input{gfx/wifi_model_error_0_95.tex}
%\input{gfx/wifi_model_error_95_100.tex}
\begin{subfigure}{0.52\textwidth}
\input{gfx2/wifi_model_error_0_95.tex}
\end{subfigure}
\begin{subfigure}{0.23\textwidth}
\input{gfx/wifiMaxErrorNN_opt0.tex}
\caption{\em \noOptEmpiric{}}
\end{subfigure}
%\begin{subfigure}{0.25\textwidth}
% \input{gfx/wifiMaxErrorNN_opt3.tex}
%\end{subfigure}
\begin{subfigure}{0.23\textwidth}
\input{gfx/wifiMaxErrorNN_opt5.tex}
\caption{\em \optPerRegion{}}
\end{subfigure}
\caption{
Comparison between different optimization strategies by examining the error (in \decibel) at each reference measurement.
The higher the number of variable parameters, the better the model resembles real world conditions.
Both figures on the right depict the highest error for each reference measurement, where full red means $\ge$ \SI{20}{\decibel}.
}
\label{fig:wifiModelError}
\end{figure}
%\begin{figure}
% \input{gfx/wifi_model_error_0_95.tex}
% %\input{gfx/wifi_model_error_95_100.tex}
% \caption{
% Comparison between different optimization strategies by examining the error (in \decibel) at each reference measurement.
% The higher the number of variable parameters, the better the model resembles real world conditions.
% }
% \label{fig:wifiModelError}
%\end{figure}
% statds:
%TXP: cnt(34) min(-67.698959) max(4.299183) range(71.998146) med(-41.961170) avg(-41.659286) stdDev(17.742294)
%EXP: cnt(34) min(0.932817) max(4.699000) range(3.766183) med(2.380410) avg(2.546959) stdDev(1.074687)
@@ -149,17 +194,34 @@
(average \SIrange{2.0}{6.2}{\decibel}), as it needs at least a certain number of measurements for each
of its regions for the optimization to converge.
\begin{figure}[b]
\input{gfx/wifi_model_error_num_fingerprints_method_5_0_90.tex}
\input{gfx/wifi_model_error_num_fingerprints_method_5_90_100.tex}
\caption{%
Impact of reducing the number of reference measurements during optimization on {\em \optPerRegion{}}.
The model's cumulative error distribution is determined by comparing the its signal strength prediction against all 121 measurements.
\begin{figure}
\begin{subfigure}{0.49\textwidth}
\input{gfx2/wifi_model_error_num_fingerprints_method_5_0_90.tex}
\end{subfigure}
\begin{subfigure}{0.49\textwidth}
\input{gfx2/wifi_model_error_num_fingerprints_method_5_90_100.tex}
\end{subfigure}
\caption{
Impact of reducing the number of reference measurements for optimizing {\em \optPerRegion{}}.
The cumulative error distribution is determined by comparing its signal strength prediction against all 121 measurements.
While using only \SI{50}{\percent} of the 121 scans has barely an impact on the error,
30 measurements (\SI{25}{\percent}) are clearly insufficient.
}%
\label{fig:wifiNumFingerprints}%
}
\label{fig:wifiNumFingerprints}
\end{figure}
%\begin{figure}[b]
% \input{gfx/wifi_model_error_num_fingerprints_method_5_0_90.tex}
% \input{gfx/wifi_model_error_num_fingerprints_method_5_90_100.tex}
% \caption{%
% Impact of reducing the number of reference measurements during optimization on {\em \optPerRegion{}}.
% The model's cumulative error distribution is determined by comparing the its signal strength prediction against all 121 measurements.
% While using only \SI{50}{\percent} of the 121 scans has barely an impact on the error,
% 30 measurements (\SI{25}{\percent}) are clearly insufficient.
% }%
% \label{fig:wifiNumFingerprints}%
%\end{figure}
Figure \ref{fig:wifiNumFingerprints} depicts the impact of reducing the number of reference measurements
during the optimization process for the {\em \optPerRegion{}} strategy.

4
tex/chapters/work.tex
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@@ -147,7 +147,7 @@
\centering
\begin{subfigure}{0.48\textwidth}
%\centering
\input{gfx/wifiop_show_optfunc_params}
\input{gfx2/wifiop_show_optfunc_params}
\caption{
Modifying \docTXP{} \mTXP{} and \docEXP{} \mPLE{}
[known position $\mPosAPVec{}$, fixed \mWAF{}] denotes a convex function.
@@ -157,7 +157,7 @@
\enskip\enskip
\begin{subfigure}{0.48\textwidth}
%\centering
\input{gfx/wifiop_show_optfunc_pos_yz}
\input{gfx2/wifiop_show_optfunc_pos_yz}
\caption{
Modifying $y$- and $z$-position [fixed $x$, \mTXP{}, \mPLE{} and \mWAF{}]
denotes a non-convex function with multiple local minima.