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reference points nochmal in optimierung erwähnt

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toni
2018-10-18 15:51:26 +02:00
parent 322678b715
commit d09f1a6882
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2
tex_review/chapters/abstract.tex
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@@ -4,7 +4,7 @@ The \add{pedestrian's} position is given by means of recursive state estimation
Our \del{rapid computation} \add{recently presented approximation} scheme of the kernel density estimation allows to find an exact estimation of the current position\add{, compared to classical methods like weighted-average}. Our \del{rapid computation} \add{recently presented approximation} scheme of the kernel density estimation allows to find an exact estimation of the current position\add{, compared to classical methods like weighted-average}.
% %
Absolute positioning information is given by a comparison between recent \docWIFI{} measurements of nearby access points and signal strength predictions. Absolute positioning information is given by a comparison between recent \docWIFI{} measurements of nearby access points and signal strength predictions.
Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access points, we use an optimization scheme based on a few reference measurements to estimate a corresponding \docWIFI{} model. Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access points, we use an optimization scheme based on a set of reference measurements to estimate a corresponding \docWIFI{} model.
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\add{This work provides three major contributions to the system.} \add{This work provides three major contributions to the system.}
\add{The most essential contribution is the novel state transition based on continuous walks along a navigation mesh, modeling only the building's walkable areas.} \add{The most essential contribution is the novel state transition based on continuous walks along a navigation mesh, modeling only the building's walkable areas.}

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tex_review/chapters/eval.tex
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@@ -90,7 +90,7 @@ The environmental parameters $\mTXP$, $\mPLE$ and $\mWAF$ need to be known befor
Nevertheless, for simplicity's sake it is common practice to use some fixed empirically chosen values, the same for every \docAPshort{}. Nevertheless, for simplicity's sake it is common practice to use some fixed empirically chosen values, the same for every \docAPshort{}.
This might already provide enough accuracy for some use-cases and buildings, but fails in complex scenarios, as discussed in section \ref{sec:intro}. This might already provide enough accuracy for some use-cases and buildings, but fails in complex scenarios, as discussed in section \ref{sec:intro}.
Therefore, instead of using a pure empiric model, we deploy an optimization scheme to find a well-suited set of parameters ($\mPosAPVec{}, \mTXP{}, \mPLE{}, \mWAF{}$) per \docAPshort{}, where $\mPosAPVec{} = (x,y,z)^T$ denotes the \docAPshort{}'s estimated position. Therefore, instead of using a pure empiric model, we deploy an optimization scheme to find a well-suited set of parameters ($\mPosAPVec{}, \mTXP{}, \mPLE{}, \mWAF{}$) per \docAPshort{}, where $\mPosAPVec{} = (x,y,z)^T$ denotes the \docAPshort{}'s estimated position.
The optimization is based on a few reference measurements $\vec{s_{\text{opt}}}$ throughout the building, e.g. every \SI{3}{} to \SI{5}{\meter} centred within a corridor and between \SI{1}{} and \SI{4}{} references per room, depending on the room's size. The optimization is based on a set of reference measurements $\vec{s_{\text{opt}}}$ throughout the building, e.g. every \SI{3}{} to \SI{7}{\meter} centred within a corridor and between \SI{1}{} and \SI{4}{} references per room, depending on the room's size.
Compared to classical fingerprinting, where reference measurements are recorded on small grids between \SI{1}{} to \SI{2}{\meter}, this highly reduces their required number and thus the overall setup-time. Compared to classical fingerprinting, where reference measurements are recorded on small grids between \SI{1}{} to \SI{2}{\meter}, this highly reduces their required number and thus the overall setup-time.
The target function to optimize the $6$ model parameters for one \docAPshort{} is given by The target function to optimize the $6$ model parameters for one \docAPshort{} is given by

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tex_review/chapters/experiments.tex
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@@ -167,8 +167,11 @@ This is more difficult using the mesh and requires the handling of baricentric c
%As described in section \ref{sec:wifi} we used \SI{42}{} WEMOS D1 mini to provide a \docWIFI{} infrastructure throughout the building. %As described in section \ref{sec:wifi} we used \SI{42}{} WEMOS D1 mini to provide a \docWIFI{} infrastructure throughout the building.
Within all Wi-Fi observations, we only consider the beacons, which are identified by their well-known MAC address. Within all Wi-Fi observations, we only consider the beacons, which are identified by their well-known MAC address.
Other transmitters like smart TVs or smartphone hotspots are ignored as they might cause estimation errors. Other transmitters like smart TVs or smartphone hotspots are ignored as they might cause estimation errors.
The references (fingerprints) we used to optimize the Wi-Fi models as well as the real position of the \docAPshort{}s (black dot) can be seen in fig. \ref{fig:apfingerprint} for ground level. The references (fingerprints) we used to optimize the Wi-Fi model as well as the real position of the \docAPshort{}s (black dot) can be seen in fig. \ref{fig:apfingerprint} for ground level.
Each reference location was scanned \SI{30}{} times ($\approx \SI{25}{\second}$ scan time) using a Motorola Nexus 6 at \SI{2.4}{GHz} band only. \add{For the complete building we defined \SI{133}{} reference points for recording Wi-Fi scans.
An evaluation of how the number of references affects the optimization can be found in \cite{Ebner-17}.
Increasing their number improves the result only up to a certain factor, which is why we have made a reasonable compromise between recording time and accuracy by distributing them every \SI{3}{\meter} to \SI{7}{\meter} from each other.}
Each reference location was then scanned \SI{30}{} times ($\approx \SI{25}{\second}$ scan time) using a Motorola Nexus 6 at \SI{2.4}{GHz} band only.
The resulting measurements were grouped per physical transmitter and aggregated to form the average signal strength per transmitter. The resulting measurements were grouped per physical transmitter and aggregated to form the average signal strength per transmitter.
The real position of every installed beacon was measured using a laser scanner. The real position of every installed beacon was measured using a laser scanner.
This allows a comparison with the optimized \docAPshort{} positions, what can also be seen in fig. \ref{fig:apfingerprint}. This allows a comparison with the optimized \docAPshort{} positions, what can also be seen in fig. \ref{fig:apfingerprint}.