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2016-07-13 10:35:44 +02:00
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@@ -55,19 +55,6 @@ where $\mObsVec_{1:t} = \mObsVec_{1}, \mObsVec_{1}, ..., \mObsVec_{t}$ is a seri
The recursive density estimation of eq. \eqref{eq:recursiveDensity} is implemented using a particle-filter with the state transition as proposal density.
This ensures valid position estimations even if a sensor is defect or is not provided by the smartphone itself.
\section{Prior Arrangements}
System setup is very easily and no fingerprinting is required.
\begin{figure}[h!]
\centering%
\includegraphics[trim=99 0 0 0, clip, width=8.2cm]{editor1.png}%
\end{figure}
\begin{itemize}
\item Map building: Grobe Beschreibung, Funktionen und Moeglichkeiten des Map Builders. bildchen
\item Adding Transmitters: geht schnell und ist total einfach
\end{itemize}
\input{chapters/setup.tex}
\input{chapters/components.tex}
\input{chapters/performance.tex}

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\section{Prior Arrangements}
The main goal for our indoor navigation system was, to reduce the initial setup time as much as possible.
As we do not rely on \docWIFI{} fingerprinting, no time consuming measurements are needed.
Instead, signal-strengths are predicted using a model, that needs the position
of each transmitter (\docAP{}, \docAPshort{}) within the building and three parameters.
For further simplification, those three parameters can be the same for all transmitters
and chosen empirically.
The system's core is based on the building's floorplan which must be known beforehand.
Using an editor, developed especially for this purpose, the provided floorplan (PDF, Images, ..)
is used to (manually) derive a new data-structure that contains each of the building's floors including
its walls, doors, stairs and transmitters (see figure \ref{fig:mapEditor}). This step consumes most
of the setup time and might be (semi-)automated in the future.
\begin{figure}
\centering%
\includegraphics[trim=99 0 0 0, clip, width=8.2cm]{editor1.png}%
\caption{One floor within a building, created by using our map-editor}
\label{fig:mapEditor}
\end{figure}
The hereby created data-structure is used to derive a graph (automatically, offline),
that is transmitted to the smartphone (once per building)
and serves as base for the transition process (see \refeq{eq:recursiveDensity}),
modelling pedestrian walking behaviour and described within the next section.