31 lines
1.4 KiB
TeX
31 lines
1.4 KiB
TeX
\section{Component Description}
|
|
|
|
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}
|
|
\input{chapters/wifi.tex}
|
|
\input{chapters/stepturn.tex}
|
|
\input{chapters/graph.tex}
|
|
|