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evaluation stand

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toni
2016-05-04 10:32:28 +02:00
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tex/chapters/experiments.tex
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@@ -18,8 +18,6 @@ All walks start with a uniform distribution (random position and heading) as pri
To allow the system to stabilize its initial state, the first few estimations are omitted from error calculations.
Even though, the error during the following few seconds is expected to be much higher than the error when starting with a well known initial position and heading.
\commentByToni{Absatz drunter muss ich noch rumschreiben.}
The measurements were recorded using a Motorola Nexus 6 and a Samsung Galaxy S5.
As the Galaxy's \docWIFI{} can not be limited to the \SI{2.4}{\giga\hertz} band only, its scans take much longer than those of the Nexus: \SI{3500}{\milli\second} vs. \SI{600}{\milli\second}.
Additionally, the Galaxy's barometer sensor provides fare more inaccurate and less frequent readings than the Nexus does.
@@ -33,20 +31,16 @@ As mentioned earlier, the position of all \docAP{}s (about 5 per floor) is known
Due to legal terms, we are not allowed to depict their positions and therefore omit this information within the figures.
Additionally, we used five \docIBeacon{}s for slight enhancements in some areas.
The empirically chosen values for \docWIFI{} were $P_{0_{\text{wifi}}} = \SI{-46}{\dBm}, \mPLE_{\text{wifi}} = \SI{2.7}{}, \mWAF_{\text{wifi}} = \SI{8}{\dB}$, and $\mPLE_{\text{ib}} = \SI{1.5}{}$ for the \docIBeacon{}s, respectively.
Due to omitting a time-consuming calibration process for those values we expect the localisation process to perform generally worse compared to standard fingerprinting methods \cite{Ville09}.
%However, incorporating prior knowledge will often compensate for those poorly chosen system parameters.
The system was tested by omitting any time-consuming calibration processes for those values.
We therefore expect the localisation process to perform generally worse compared to standard fingerprinting methods \cite{Ville09}.
However, incorporating prior knowledge and smoothing will often compensate for those poorly chosen system parameters.
\commentByToni{Hier eure noetigen Werte eintragen.}
As uncertainties we used $\sigma_\text{wifi} = \sigma_\text{ib} = 8.0$, both growing with each measurement's age.
For the filtering we used $\sigma_\text{wifi} = \sigma_\text{ib} = 8.0$ as uncertainties, both growing with each measurement's age.
While the pressure change was assumed to be \SI{0.105}{$\frac{\text{\hpa}}{\text{\meter}}$}, all other barometer-parameters are determined automatically (see \ref{sec:sensBaro}).
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 in \refeq{eq:transSimple}, \refeq{eq:transShortestPath} and \refeq{eq:transMultiPath} was \SI{25}{\degree}.
Edges departing from the pedestrian's destination are downvoted using $\mUsePath = 0.9$.
%
% all paths we evaluated
\begin{figure}
\input{gfx/eval/paths}
@@ -58,6 +52,8 @@ Edges departing from the pedestrian's destination are downvoted using $\mUsePath
\end{figure}
%kurz zeigen das activity recognition was bringt
%Smoothing mit großen lag kann die zeitliche information schwer halten. das liegt hauptsächlich daran, das im smoothing nur die relativen positionsinfos genutzt werden. das wi-fi wird nicht beachtet und deswegen können absolute justierungen der position (sprünge) nur sehr schlecht abgefedert werden.