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worked on eval and transition
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kazu
2016-05-03 13:01:02 +02:00
parent 5bde96df3a
commit ce1a1b4d99
5 changed files with 132 additions and 86 deletions
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10
tex/chapters/system.tex
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@@ -27,14 +27,17 @@ In context of indoor localisation, the hidden state $\mStateVec$ is defined as f
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). Further, the observation is given by
%
\begin{equation}
\mObsVec = (\mRssiVec_\text{wifi}, \mRssiVec_\text{ib}, \mObsHeading, \mObsSteps, \mObsPressure, x) \enspace,
\mObsVec = (\mRssiVec_\text{wifi}, \mRssiVec_\text{ib}, \mObsHeading, \mObsSteps, \mObsPressure, \mObsActivity) \enspace,
\end{equation}
%
covering all relevant sensor measurements.
Here, $\mRssiVec_\text{wifi}$ and $\mRssiVec_\text{ib}$ contain the measurements of all nearby \docAP{}s (\docAPshort{}) and \docIBeacon{}s, respectively.
$\mObsHeading$ and $\mObsSteps$ describe the relative angular change and the number of steps detected for the pedestrian.
$\mObsPressure$ is the relative barometric pressure with respect to a fixed reference.
Finally, $x$ \commentByLukas{Vermutlich gerade nur Platzhalter. Aber x ueberschneidet sich mit dem x der Position. Wie waers mit $\Omega$} contains the activity, currently estimated for the pedestrian, which is one of: unknown, standing, walking or walking stairs.
Finally, $\mObsActivity$
\commentByLukas{Vermutlich gerade nur Platzhalter. Aber x ueberschneidet sich mit dem x der Position. Wie waers mit $\Omega$}
\commentByFrank{ja war ein platzhalter, hatte auch Omega vorgesehen} contains the activity, currently estimated for the pedestrian, which is one of:
unknown, standing, walking, walking stairs up or walking stairs down.
The probability density of the state evaluation is given by
%
@@ -51,7 +54,8 @@ The probability density of the state evaluation is given by
%
and therefore similar to \cite{Ebner-16}.
Here, we assume a statistical independence of all sensors and 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{}.
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{}.