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removed some words for a better text-flow
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FrankE
2016-02-25 11:02:03 +01:00
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tex/chapters/system.tex
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@@ -26,7 +26,7 @@
The recursive part of the density estimation contains all information up to time $t-1$.
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.
As proven in \cite{Koeping14-PSA}, we may include the observation $\mObsVec_{t-1}$ into the state transition.
Containing all relevant sensor measurements to evaluate the current state, the observation vector is defined as follows:
%
@@ -37,7 +37,7 @@
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.
Finally, $\mObsPressure$ is the relative barometric pressure with respect to some fixed point in time.
Finally, $\mObsPressure$ is the relative barometric pressure with respect to a fixed reference.
For further information on how to incorporate such highly different sensor types,
one should refer to the process of probabilistic sensor fusion \cite{Khaleghi2013}.
By assuming statistical independence of all sensors, the probability density of the state evaluation is given by