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FrankE
2017-05-02 13:34:13 +02:00
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tex/chapters/system.tex
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@@ -21,7 +21,7 @@
The smartphone's accelerometer, gyroscope, magnetometer, GPS- and \docWIFI{}-module provide
the observations for both, the transition and the following evaluation step to infer the hidden state,
namely the pedestrian's location and heading
\cite{Ebner-16, Fetzer-16}.
\cite{Ebner2016OPN, Fetzer2016OMC}.
This hidden state $\mStateVec$ is given by
@@ -38,13 +38,14 @@
The corresponding observation vector is defined as
%
\begin{equation}
\mObsVec = (\mRssiVecWiFi{}, \mObsSteps, \mObsHeadingRel, \mObsHeadingAbs, \mObsGPS) \enspace.
\mObsVec = (\mRssiVecWiFi{}, \mObsSteps, \mObsHeadingRel, \mObsHeadingAbs, \mPressure, \mObsGPS) \enspace.
\end{equation}
%
$\mRssiVecWiFi$ contains the signal strength measurements of all \docAP{}s (\docAPshort{}s) currently visible to the smartphone,
$\mObsSteps$ describes the number of steps detected since the last filter-step,
$\mObsHeadingRel$ the (relative) angular change since the last filter-step,
$\mObsHeadingAbs$ the current, vague absolute heading and
$\mObsHeadingAbs$ the current, vague absolute heading,
$\mPressure$ the ambient pressure in hPa and
$\mObsGPS = ( \mObsGPSlat, \mObsGPSlon, \mObsGPSaccuracy)$ the current location (if available) given by the GPS.
@@ -53,14 +54,24 @@
\begin{equation}
p(\vec{o}_t \mid \vec{q}_t) =
p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}\enskip
p(\vec{o}_t \mid \vec{q}_t)_\text{gps}
p(\vec{o}_t \mid \vec{q}_t)_\text{gps}\enskip
p(\vec{o}_t \mid \vec{q}_t)_\text{abshead}\enskip
p(\vec{o}_t \mid \vec{q}_t)_\text{activity}\enskip
\enspace.
\label{eq:evalDensity}
\end{equation}
%
Absolute location information is provided by $p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}$ and
$p(\vec{o}_t \mid \vec{q}_t)_\text{gps}$, if available.
The vague absolute heading provided by
the smartphone's magnetometer is included using a simple heuristic for
$p(\vec{o}_t \mid \vec{q}_t)_\text{abshead}$. Finally, the barometer is used
to distinguish between normal walking and climbing stairs within
$p(\vec{o}_t \mid \vec{q}_t)_\text{activity}$.
%
The remaining observations, derived from aforementioned smartphone sensors,
namely: detected steps, relative- and absolute heading are
namely: detected steps, and relative heading are
used within the transition model, where potential movements
$p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$
are not only constrained by the buildings floorplan but also by
@@ -68,17 +79,17 @@
As this work focuses on \docWIFI{} optimization, not all parts of
the localization system are discussed in detail.
For missing explanations please refer to \cite{Ebner-16}.
For missing explanations please refer to \cite{Ebner2016OPN}.
%
{\bf Since then}, absolute heading and GPS have been added as additional sensors
Compared to this reference, absolute heading and GPS have been added as additional sensors
to further enhance the localization. Their values are incorporated by simply
comparing the sensor readings against a distribution that models the sensor's uncertainty.
%\todo{neues resampling? je nach dem was sich noch in der eval zeigt}
As GPS will only work outdoors, e.g. when moving from one building into another,
the system's absolute position indoors is solely provided by the \docWIFI{} component.
Therefore its crucial for this component to provide location estimations
the system's absolute position indoors is solely provided by \docWIFI{}.
Therefore its crucial for this component to supply location estimations
that are as accurate as possible, while ensuring fast setup and
maintenance times.