56 lines
3.6 KiB
TeX
56 lines
3.6 KiB
TeX
\section{Experiments}
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All upcoming experiments were carried out on four floors of a \SI{77}{m} x \SI{55}{m} sized faculty building.
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It includes several staircases and elevators and has a ceiling height of about \SI{3}{m}.
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Nevertheless, the grid was generated for the complete campus and thus outdoor areas like the courtyard are also walkable.
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As Wi-Fi is attenuated by obstacles and walls, it does not provide a consistent quality over the complete building.
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In fig. \ref{} we illustrate the quality obtained by the wall attenuation factor model presented earlier.
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Here, green indicates a high coverage and thus a good quality for localisation, while red does the opposite.
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To obtain this information we measured Wi-Fi at $666$ different points and interpolated the results as described in \cite{}.
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As mentioned before, we omit any time-consuming calibration processes and use the same values for all access-points. That would be $P_{0_{\text{wifi}}} = \SI{-46}{\dBm}, \mPLE_{\text{wifi}} = \SI{2.7}{}, \mWAF_{\text{wifi}} = \SI{8}{\dB}$.
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The position of the access-points (about five per floor) is known beforehand.
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Due to legal terms, we are not allowed to depict their positions and therefore omit this information within the figures.
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We arranged three distinct walks (see also fig. \ref{}).
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The measurements for the walks were recorded using a Motorola Nexus 6 at 2.4 GHz band only.
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The computation was done offline as described in algorithm \ref{}.
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For each walk we deployed $xx$ MC runs using 5000 Particles.
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Instead of an initial position and heading, all walks start with a uniform distribution (random position and heading) as prior.
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For the filtering we used $\sigma_\text{wifi} = 8.0$ as uncertainties, both growing with each measurement's age.
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While the pressure change was assumed to be \SI{0.105}{$\frac{\text{\hpa}}{\text{\meter}}$}, all other barometer-parameters are determined automatically.
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The step size $\mStepSize$ for the transition was configured to be \SI{70}{\centimeter} with an allowed derivation of \SI{10}{\percent}. The heading deviation was set to \SI{25}{\degree}.
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KLD with normal dist and kernel density drawing from grid.
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The ground truth is measured by recording a timestamp at marked spots on the walking route. When passing a marker, the pedestrian clicked a button on the smartphone application.
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Between two consecutive points, a constant movement speed is assumed.
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Thus, the ground truth might not be \SI{100}{\percent} accurate, but fair enough for error measurements.
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The approximation error is then calculated by comparing the interpolated ground truth position with the current estimation \cite{Fetzer2016OMC}.
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% allgemeine infos über pfade und gebäude. wo
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% bild: mit pfaden drauf und eventl. wifi qualität in jeweiligen bereichen? (kann frank das)
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% gewählte parameter (auch mal die optimieren wifi parameter testen)
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% wie für die kld gezogen? begründen warum wir nun keine parzenschätzung machen (weil ähnliche ergebnisse)
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% ground truth
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% maß für die streuung der verteilung (diversity von partikeln)
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% zeigen das es stucken verhindert (eventl. hier eine andere aufnahme die mitten drinnen stecken bleibt)
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% bild: stucken im raum + nicht mehr stucken im raum
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% zeigen das schlechtes wi-fi (zu hohe diversity) behoben wird.
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% bild: lauf auf der rechten seite des gebäudes zeige mit und ohne wifi faktor (schlechtes wifi einzeichnen)
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% zeigen das immpf nicht viel schlechter als normaler pf (ohne stucken) ist.
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% bild: er schafft es nicht die treppe rauf + er schafft es immpf + er schafft es normal filter
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% gegenüberstellung aller pfade und werte in tabelle
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