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toni
2016-05-05 13:40:03 +02:00
parent f7b50a17ce
commit 19bca6b5b9
9 changed files with 84 additions and 166 deletions
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tex/chapters/system.tex
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@@ -18,7 +18,8 @@ Therefore, a Bayes filter that satisfies the Markov property is used to calculat
\end{equation}
%
Here, the previous observation $\mObsVec_{t-1}$ is included into the state transition \cite{Koeping14-PSA}.
For approximating eq. \eqref{equ:bayesInt} by means of MC methods, the transition is used as proposal distribution, also known as CONDENSATION algorithm \cite{isard1998smoothing}.
For approximating eq. \eqref{equ:bayesInt} by means of MC methods, the transition is used as proposal distribution, also known as CONDENSATION algorithm \cite{isard1998smoothing}.
The handle the phenomenon of weight degeneracy we additionally apply a resampling step.
In context of indoor localisation, the hidden state $\mStateVec$ is defined as follows:
\begin{equation}
@@ -36,9 +37,7 @@ 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, $\mObsActivity$
\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:
Finally, $\mObsActivity$ 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
@@ -60,7 +59,3 @@ The barometer information is evaluated using $p(\vec{o}_t \mid \vec{q}_t)_\text{
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{}.