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final version paper

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toni
2017-05-12 16:37:19 +02:00
parent 500c93068c
commit 5779d9496d
7 changed files with 39 additions and 29 deletions
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6
tex/chapters/system.tex
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@@ -35,7 +35,7 @@ All relevant sensor measurements are incorporated into the observation $\mObsVec
\mObsVec = (\mObsHeading, \mObsSteps, \mRssiVec_\text{wifi}, \mObsPressure) \enspace,
\end{equation}
%
Here, $\mObsHeading$ and $\mObsSteps$ describe the relative angular change and the number of steps detected for the pedestrian.
Here, $\mObsHeading$ and $\mObsSteps$ describe the relative angular change and the number of steps detected for the pedestrian between two consecutive timesteps.
Further, $\mRssiVec_\text{wifi}$ contains the measurements of all nearby \docAP{}s (\docAPshort{}).
Finally, $\mObsPressure$ is the relative barometric pressure with respect to a fixed reference.
@@ -57,13 +57,13 @@ We assume a statistical independence of all sensors. The probability density of
The smartphone's barometer is used to infer the likeliness of the current $z$-location in $p(\vec{o}_t \mid \vec{q}_t)_\text{baro}$ and thus enables to walk stairs or to drive elevators.
Here, every predicted relative pressure $\mState_t^{\mStatePressure}$ is compared with the observed one $\mObs_t^{\mObsPressure}$ using a normal distribution.
The state's relative pressure prediction $\mStatePressure$ is estimated within each transition from $\mStateVec_{t-1}$ to $\mStateVec_t$ by tracking the pressure between every height-change on the $z$-axis.
The state's relative pressure prediction $\mStatePressure$ is estimated within each transition from $\mStateVec_{t-1}$ to $\mStateVec_t$ by tracking the pressure between every height-change on the $z$-axis \cite{Fetzer2016OMC}.
Absolute position information is given by $p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}$ for \docWIFI{}.
We are using the wall attenuation factor model based on Friis transmission equation to predict an \docAP{}'s (\docAPshort{}) signal strength at an arbitrary position $\fPos{\mStateVec_t} = (x, y,z)^T$.
This predicted signal strength is then matched against the current observation $\mObs_t^{\mRssiVec_\text{wifi}}$ received from this particular \docAPshort{}, providing a likelihood of the pedestrian being at $\fPos{\mStateVec_t}$.
The positions of detected \docAPshort{}'s are known beforehand.
The main advantage of this approach is that no time-consuming initial calibration phase and updates in case of infrastructural changes are needed.
The main advantage of this approach is that no time-consuming initial calibration phase and updates in case of infrastructural changes are needed \cite{Ebner-17}.
%Barometer
%Due to noisy sensors, we calculate the average $\overline{\mObsPressure}$ of several sensor readings and the sensor's uncertainty $\sigma_\text{baro}$.