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toni first_draft

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Toni
2016-02-15 15:27:00 +01:00
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10
tex/chapters/system.tex
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@@ -24,11 +24,9 @@
where $x, y, z$ represent the position in 3D space, $\mObsHeading$ the user's heading and $\mStatePressure$ the
relative pressure prediction in hectopascal (hPa).
The recursive part of the density estimation contains all information up to time $t$.
Furthermore, the state transition models the pedestrian's movement based on random walks on graphs,
described in section \ref{sec:trans}.
Furthermore, the state transition models the pedestrian's movement as described in section \ref{sec:trans}.
%It should be noted, that we also include the current observation $\mObsVec_{t}$ in it.
Differing from the usual notation, the state transition also includes the current observation $\mObsVec_{t}$.
\commentByFrank{brauchen wir hier noch das cite?}
Differing from the usual notation, the state transition also includes the current observation $\mObsVec_{t}$ \cite{Koeping14}.
Containing all relevant sensor measurements to evaluate the current state, the observation vector is defined as follows:
%
@@ -38,8 +36,7 @@
%
where $\mRssiVec_\text{wifi}$ 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.
of steps detected for the pedestrian.
Finally, $\mObsPressure$ is the relative barometric pressure with respect to some fixed point in time.
For further information on how to incorporate such highly different sensor types,
one should refer to the process of probabilistic sensor fusion \cite{Khaleghi2013}.
@@ -67,6 +64,5 @@
a particle filter is chosen as approximation of the posterior distribution.
Within this work the state transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t})$ is used as proposal distribution,
what is also known as CONDENSATION algorithm \cite{Isard98:CCD}.
\commentByFrank{caps? fehlt da noch was?}