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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
3 changed files with 7 additions and 4 deletions
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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}.
%
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.
%
\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.}

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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{}.
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.
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.
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.
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.
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.
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.
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.
\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 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}.