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intro component description

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Toni
2016-07-11 13:44:29 +02:00
parent 9f4547b2c1
commit 403a2f84fe
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22
competition/tex/chapters/components.tex
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@@ -1,6 +1,26 @@
\section{Component Description}
Our indoor localisation solely uses the sensors provided by almost each commodity smartphone.
As described above, our indoor localisation solely uses the sensors provided by almost each commodity smartphone.
By assuming statistical independence of all sensors, the probability density of the state evaluation of eq. \eqref{eq:recursiveDensity} is given by
%
\begin{equation}
%\begin{split}
p(\vec{o}_t \mid \vec{q}_t) =
p(\vec{o}_t \mid \vec{q}_t)_\text{baro}
\,p(\vec{o}_t \mid \vec{q}_t)_\text{ib}
\,p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}
\enspace.
%\end{split}
\label{eq:evalBayes}
\end{equation}
%
Here, every single component refers to a probabilistic sensor model.
The barometer information is evaluated using $p(\vec{o}_t \mid \vec{q}_t)_\text{baro}$,
whereby absolute position information is given by $p(\vec{o}_t \mid \vec{q}_t)_\text{ib}$ for
\docIBeacon{}s and by $p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}$ for \docWIFI{}.
Compared to other state-of-the-art system, the step- and turn-detection is not incorporated into the evaluation step.
In our approach it stabilizes and improves the sampling of states $\vec{q}$ into moving more realistically. The transition step is the carried out using random walks on a graph, which is built offline, and uses the building's floorplan \cite{ebner-16}.
\input{chapters/barometer.tex}

3
competition/tex/chapters/introduction.tex
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@@ -32,8 +32,7 @@ where $\mObsVec_{1:t} = \mObsVec_{1}, \mObsVec_{1}, ..., \mObsVec_{t}$ is a seri
%
where $x, y, z$ represent the position in 3D space, $\mStateHeading$ the user's heading and $\mStatePressure$ the relative atmospheric pressure prediction in hectopascal (hPa).
The recursive part of the density estimation contains all information up to time $t-1$.
Furthermore, the state transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$ models the pedestrian's movement and is carried out using random walks on a graph, which is built offline, and uses the building's floorplan \cite{ebner-16}.
Furthermore, the state transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$ models the pedestrian's movement, whereby the evaluation provides a likelihood for every sensor.
Containing all relevant sensor measurements to evaluate the current state, the observation vector is defined as follows:
%
\begin{equation}