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changed some GFX

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fixed some TeX issues
This commit is contained in:
FrankE
2016-02-21 21:05:34 +01:00
parent 3fd0db82fe
commit 44504f0a59
16 changed files with 223513 additions and 76119 deletions
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11
tex/chapters/sensors.tex
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@@ -16,8 +16,10 @@
%
\begin{figure}
\include{gfx/baro/baro_setup_issue}
\caption{Sometimes the barometer provides erroneous \SI{}{\hpa} readings during the first seconds. Those
need to be omitted before $\sigma_\text{baro}$ and $\overline{\mObsPressure}$ are estimated.}
\caption{Sometimes the smartphone's barometer (here: Motorola Nexus 6) provides erroneous pressure readings
during the first seconds. Those need to be omitted before $\sigma_\text{baro}$ and
$\overline{\mObsPressure}$ are estimated.
\commentByFrank{fixed}}
\label{fig:baroSetupError}
\end{figure}
%
@@ -49,7 +51,7 @@
\subsection{Wi-Fi \& iBeacons}
Additional absolute location hints, are provided by the smartphone's \docWIFI{} and \docIBeacon{} component,
Additional absolute location hints are provided by the smartphone's \docWIFI{} and \docIBeacon{} component,
measuring the signal-strengths of nearby transmitters. As the positions of both \docAP{}s and \docIBeacon{}s
are known beforehand, we compare each measurement with its corresponding signal strength prediction using
the wall-attenuation-factor model \cite{Ebner-15}. This prediction depends on the 3D distance $d$ from the
@@ -89,7 +91,8 @@
impoverishment due to a small measurement noise. This happens since accurate observations result in high peaks
of the evaluation density and therefore the proposal density is not able to sample outside that peak \cite{Isard98:CCD}.
Additionally, erroneous or delayed measurements from absolute positioning sensors like \docWIFI{} may lead to misplaced turns.
This causes a downvoting of particles with increased heading deviation.
This causes a downvoting of all states $\mStateVec_t$ with increased heading deviation.
\commentByFrank{so besser?: downvoting of states statt particles}
Therefore, we incorporate the turn-detection, as well as the related step-detection, directly into the state transition
$p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$, which leads to a more directed sampling instead of a truly random one.