toni/IPIN2016
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

changed chapter order (filtering)

Browse Source
This commit is contained in:
kazu
2016-05-05 10:24:00 +02:00
parent a6790c3db3
commit 9d3efd10c7
1 changed files with 79 additions and 81 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
tex/chapters/filtering.tex
View File

@@ -1,14 +1,12 @@
%\section{Filtering} \section{Filtering}
%
% \label{sec:filtering} \commentByFrank{eval und transition tauschen von der reihenfolge?}
%
% \commentByToni{Bin mir nicht sicher ob wir diese Section überhaupt brauchen. Könnte man bestimmt auch einfach unter Section 3 packen. Aber dann können wir ungestört voneinander schreiben.} \subsection{Evaluation}
%
\section{Evaluation}
\commentByFrank{brauchen wir hier noch was (kurze einleitung) oder passt das so?} \commentByFrank{brauchen wir hier noch was (kurze einleitung) oder passt das so?}
\subsection{Barometer} \subsubsection{Barometer}
\label{sec:sensBaro} \label{sec:sensBaro}
% %
The probability of currently residing on a floor is evaluated using the smartphone's barometer. The probability of currently residing on a floor is evaluated using the smartphone's barometer.
@@ -43,7 +41,7 @@
% %
% %
% %
\subsection{Wi-Fi \& iBeacons} \subsubsection{Wi-Fi \& iBeacons}
% %
The smartphone's \docWIFI{} and \docIBeacon{} component provides an absolute location estimation by 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 measuring the signal-strengths of nearby transmitters. The positions of detected \docAP{}s (\docAPshort{}) and \docIBeacon{}s
@@ -77,8 +75,8 @@
\section{Transition} \subsection{Transition}
\label{sec:transition} \label{sec:transition}
The transition-distribution $p(\mStateVec_{t} \mid \mStateVec_{t-1})$ is sampled via random walks on a graph The transition-distribution $p(\mStateVec_{t} \mid \mStateVec_{t-1})$ is sampled via random walks on a graph
$G=(V,E)$, which is generated from the buildings floorplan \cite{Ebner-16}. $G=(V,E)$, which is generated from the buildings floorplan \cite{Ebner-16}.
@@ -94,14 +92,14 @@
\commentByFrank{ist das verstaendlich oder schon zu kurz?} \commentByFrank{ist das verstaendlich oder schon zu kurz?}
\subsection{Pedestrian's Destination} \subsubsection{Pedestrian's Destination}
We assume the pedestrian's desired destination to be known beforehand. This prior knowledge is incorporated 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) 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 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, \cite{Ebner-16}. The underlying shortest-path uses Dijkstra's algorithm with special weight (distance) metric,
considering special architectural facts: considering special architectural facts:
\subsection{Architectural Facts} \subsubsection{Architectural Facts}
Normally, the shortest-path calculated for a narrow grid would stick unnaturally close to obstacles like walls. 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}: 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 Each vertex's distance from the nearest wall is used to artificially increase the edge-weight and thus prevent the shortest-path
@@ -109,7 +107,7 @@
and favoured by decreasing their edge-weight. and favoured by decreasing their edge-weight.
\subsection{Step- \& Turn-Detection} \subsubsection{Step- \& Turn-Detection}
Steps and turns are detected using the smartphone's IMU, implemented as described in \cite{Ebner-15}. 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 $\gDist$ 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: by assuming a fixed step-size with some deviation:
@@ -138,7 +136,7 @@
While the distribution \refeq{eq:transHeading} does not integrate to $1.0$ due to circularity of angular 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 $\sigma^2$. data, in our case, the normal distribution can be assumed as sufficient for small enough $\sigma^2$.
\subsection{Activity-Detection} \subsubsection{Activity-Detection}
Additionally we perform a simple activity detection for the pedestrian, able to distinguish between several actions Additionally we perform a simple activity detection for the pedestrian, able to distinguish between several actions
$\mObsActivity \in \{ \text{unknown}, \text{standing}, \text{walking}, \text{stairs\_up}, \text{stairs\_down} \}$. $\mObsActivity \in \{ \text{unknown}, \text{standing}, \text{walking}, \text{stairs\_up}, \text{stairs\_down} \}$.