31 lines
1.4 KiB
TeX
31 lines
1.4 KiB
TeX
\section{Component Description}
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As described above, our indoor localisation solely uses the sensors provided by almost each commodity smartphone.
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By assuming statistical independence of all sensors, the probability density of the state evaluation of eq. \eqref{eq:recursiveDensity} is given by
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%
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\begin{equation}
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%\begin{split}
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p(\vec{o}_t \mid \vec{q}_t) =
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p(\vec{o}_t \mid \vec{q}_t)_\text{baro}
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\,p(\vec{o}_t \mid \vec{q}_t)_\text{ib}
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\,p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}
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\enspace.
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%\end{split}
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\label{eq:evalBayes}
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\end{equation}
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%
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Here, every single component refers to a probabilistic sensor model.
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The barometer information is evaluated using $p(\vec{o}_t \mid \vec{q}_t)_\text{baro}$,
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whereby absolute position information is given by $p(\vec{o}_t \mid \vec{q}_t)_\text{ib}$ for
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\docIBeacon{}s and by $p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}$ for \docWIFI{}.
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Compared to other state-of-the-art systems, step- and turn-detection are not incorporated into the evaluation step.
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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}.
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\input{chapters/barometer.tex}
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\input{chapters/wifi.tex}
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\input{chapters/stepturn.tex}
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\input{chapters/graph.tex}
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\input{chapters/smoothing.tex}
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