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toni changes in franks part

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toni
2016-02-23 15:34:00 +01:00
parent 8889c4cf83
commit 822f71f633
4 changed files with 19 additions and 21 deletions
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tex/chapters/system.tex
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@@ -24,7 +24,7 @@
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).
The recursive part of the density estimation contains all information up to time $t-1$.
Furthermore, the state transition models the pedestrian's movement as described in section \ref{sec:trans}.
Furthermore, the state transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$ 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.
As \cite{Koeping14-PSA} has proven, we are able to include the observation $\mObsVec_{t-1}$ into the state transition.
@@ -62,7 +62,7 @@
Therefore, numerical solutions like Gaussian filters or the broad class of Monte Carlo methods are deployed \cite{sarkka2013bayesian}.
Since we assume indoor localisation to be a time-sequential, non-linear and non-Gaussian process,
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,
Within this work the state transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$ is used as proposal distribution,
what is also known as CONDENSATION algorithm \cite{Isard98:CCD}.