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removed some subsections
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kazu
2016-05-05 10:52:47 +02:00
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24
tex/chapters/filtering.tex
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@@ -1,14 +1,13 @@
\section{Filtering}
%\section{Filtering}
\commentByFrank{eval und transition tauschen von der reihenfolge?}
\subsection{Evaluation}
\label{sec:eval}
\commentByFrank{brauchen wir hier noch was (kurze einleitung) oder passt das so?}
\subsubsection{Barometer}
\label{sec:sensBaro}
%
%\subsubsection{Barometer}
%\label{sec:sensBaro}
The probability of currently residing on a floor is evaluated using the smartphone's barometer.
Environmental influences are circumvented by using relative pressure values instead of absolute ones.
To reduce the impact of noisy sensors, we calculate the average $\overline{\mObsPressure}$ of several
@@ -41,8 +40,8 @@
%
%
%
\subsubsection{Wi-Fi \& iBeacons}
%
%\subsubsection{Wi-Fi \& iBeacons}
The smartphone's \docWIFI{} and \docIBeacon{} component provides an absolute location estimation by
measuring the signal-strengths of nearby transmitters. The positions of detected \docAP{}s (\docAPshort{}) and \docIBeacon{}s
are known beforehand. Using the wall-attenuation-factor signal strength prediction model \cite{Ebner-15}, we are able to
@@ -92,14 +91,14 @@
\commentByFrank{ist das verstaendlich oder schon zu kurz?}
\subsubsection{Pedestrian's Destination}
%\subsubsection{Pedestrian's Destination}
We assume the pedestrian's desired destination to be known beforehand. This prior knowledge is incorporated
during the random walk using $p(\mEdgeAB)_\text{path}$, which is a simple heuristic, favouring movements (edges)
approaching his chosen destination with a ratio of $0.9:0.1$ over those, departing from the destination
\cite{Ebner-16}. The underlying shortest-path uses Dijkstra's algorithm with special weight (distance) metric,
considering special architectural facts:
\subsubsection{Architectural Facts}
%\subsubsection{Architectural Facts}
Normally, the shortest-path calculated for a narrow grid would stick unnaturally close to obstacles like walls.
To ensure realistic (human like) path estimations, we include architectural knowledge within Dijkstra's edge-weight function \cite{Ebner-16}:
Each vertex's distance from the nearest wall is used to artificially increase the edge-weight and thus prevent the shortest-path
@@ -107,7 +106,7 @@
and favoured by decreasing their edge-weight.
\subsubsection{Step- \& Turn-Detection}
%\subsubsection{Step- \& Turn-Detection}
Steps and turns are detected using the smartphone's IMU, implemented as described in \cite{Ebner-15}.
The number of steps detected since the last transition is used to estimate the to-be-walked distance $\gDist$
by assuming a fixed step-size with some deviation:
@@ -136,8 +135,7 @@
While the distribution \refeq{eq:transHeading} does not integrate to $1.0$ due to circularity of angular
data, in our case, the normal distribution can be assumed as sufficient for small enough $\sigma^2$.
\subsubsection{Activity-Detection}
%\subsubsection{Activity-Detection}
Additionally we perform a simple activity detection for the pedestrian, able to distinguish between several actions
$\mObsActivity \in \{ \text{unknown}, \text{standing}, \text{walking}, \text{stairs\_up}, \text{stairs\_down} \}$.
Likewise, this knowledge is evaluated when walking the grid: Edges $\mEdgeAB$ matching the currently detected

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tex/chapters/system.tex
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\section{Recursive State Estimation}
%\section{Recursive State Estimation}
\section{Filtering}
\label{sec:filtering}
As mentioned before, most smoothing methods require a preceding filtering.
In our previous work \cite{Ebner-16}, we consider indoor localisation as a time-sequential, non-linear and non-Gaussian state estimation problem.