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\subsection{Barometer}
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used if available
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relative positioning (z)
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relative to the first few measurements
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also used to determine uncertainty
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If available, the Smartphone's barometer is used to infer the likelyness of the current $z$-location%
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%
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As ambient pressure readings are highly influenced by environmental conditions
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like the weather, time-of-day and others \cite{Muralidharan14-BPS},
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we use relative pressure instead of absolute ones.%
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%
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Due to noisy sensors, more than one reading is used to estimate this relative base.
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The usual setup time of a navigation-system (route calculation, etc.)
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is used to average all barometer readings during this timeframe.
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This average $\overline{\mObsPressure}$ serves as relative base.
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Furthermore, we estimate the sensor's uncertainty $\sigma_\text{baro}$ for later use within the evaluation step.%
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%
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During each transition from $\mStateVec_{t-1}$ to $\mStateVec_t$, we need a corresponding, relative
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pressure prediction $\mStatePressure$ which is adjusted whenever a $z$-change happens within the transition.
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%
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% \begin{equation}
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% \mState_{t}^{\mStatePressure} = \mState_{t-1}^{\mStatePressure} + \Delta z \cdot b
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% ,\enskip
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% \Delta z = \mState_{t-1}^{z} - \mState_{t}^z
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% ,\enskip
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% b \in \R
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% \enspace .
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% \label{eq:baroTransition}
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% \end{equation}
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33
competition/tex/chapters/components.tex
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33
competition/tex/chapters/components.tex
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\section{Component Description}
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Our indoor localisation solely uses the sensors provided by almost each commodity smartphone.
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The readings of all those sensors are fused using recursive density estimation, directly on the phone:
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\commentByFrank{state beschreiben: x, y, z, heading. oder machst du das schon weiter oben? dann kann vermutlicha uch die formel hier weg}
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\begin{equation}
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\arraycolsep=1.2pt
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\begin{array}{ll}
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&p(\mStateVec_{t} \mid \mObsVec_{1:t}) \propto\\
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&\underbrace{p(\mObsVec_{t} \mid \mStateVec_{t})}_{\text{evaluation}}
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\int \underbrace{p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})}_{\text{transition}}
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\underbrace{p(\mStateVec_{t-1} \mid \mObsVec_{1:t-1})d\vec{q}_{t-1}}_{\text{recursion}} \enspace,
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\end{array}
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\label{eq:recursiveDensity}
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\end{equation}
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\docWIFI{} and (if available) \docIBeacon{}s serve as absolute positioning component. If the smartphone provides
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a barometer, its measurements are used as an additional, relative verification for the current $z$-component
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of the pedestrian's location.
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The transition in \refeq{eq:recursiveDensity} is carried out using random walks on a graph, which is built offline, and uses
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the building's floorplan. During the localisation process, the smartphone's IMU (accelerometer, gyroscope) is used to constrain the random walk
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in both, distance and heading.
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The recursive density estimation is implemented using a particle-filter.
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\input{chapters/barometer.tex}
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\input{chapters/wifi.tex}
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\input{chapters/stepturn.tex}
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\input{chapters/graph.tex}
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@@ -1,7 +1,31 @@
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\subsection{Transition}
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a graph based on the building's floorplan
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uses prior knowledge (floorplan + desired destination [if known])
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uses random-walks to perform the transition
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uses imo (acc,gyro) for the random-walk (distance/direction)
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calculated offline
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To enhance the quality of the proposal distribution, the transition step is
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based on a \SI{20}{\centimeter}-gridded graph $G = (V,E)$
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with vertices $v_i \in V$ and undirected edges $e_{i,j} \in E$
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derived from the buildings floorplan. This ensures that only valid
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movements can be sampled from the previous state.%
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\todo{wenn platz dann bild?}
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The graph is built once and offline using the floorplan created by our editor.
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Besides realistic stairwells, additional semantic information (e.g. doors)
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can be included. Hereafter, the built graph is transmitted to the smartphone
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and is used during the online phase.
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If the pedestrian's destination is know beforehand, this information can
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be included as prior knowledge into the transition step. A shortest-path
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calculation imposes additional constraints to the transition by favouring
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movements that approach the desired destination (pedestrian sticking to the shortest path)
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over movements that depart from the destination.
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To ensure that the calculated shortest path is realistic (resembled human walking paths)
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each node within the graph contains a weight, denoting the likelyhood for being visited
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by the pedestrian. Using this approach, nodes near to walls receive a lower likelyhood.
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During the path-calculation this importance is used to artificially increase/decrease the
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weight $\delta(\mEdgeAB)$ between two nodes. This ensures that the resulting path is
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farther away from obstacles and looks much more realistic
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\todo{wenn platz dann bild?}
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@@ -5,7 +5,11 @@
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\item aus welchen arbeiten fuegt sich das system zusammen?
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\item grober ueberblick ueber die einzelnen komponenten und sensoren
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\item modulare uebersicht ueber das gesamte system. (denis bild + smoothing und prior)
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\item particle filter mit formeluebersicht und was fusioniert wird
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\item particle filter mit formeluebersicht und was fusioniert wird
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\begin{figure}[h!]
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\centering%
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\includegraphics[trim=99 0 0 0, clip, width=8.2cm]{editor1.png}%
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\end{figure}
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\end{itemize}
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\cite{ebner-15}
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@@ -19,13 +23,9 @@ System setup is very easily and no fingerprinting is required.
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\end{itemize}
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\section{Component Description}
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Zu jeder Componente eine kurze Beschreibung welche die Grundfunktionen und den Wert innerhalb des Systems deutlich hervorhebt. Details werden dann referenziert.
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\input{chapters/barometer.tex}
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\input{chapters/wifi.tex}
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\input{chapters/stepturn.tex}
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\input{chapters/graph.tex}
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\input{chapters/components.tex}
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\begin{itemize}
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\item Fixed-lag smoother
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@@ -1,7 +1,11 @@
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\subsection{Step- and Turn-Detection}
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used for the transition step
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uses the smartphone's imu (acc, gyro)
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simple step detection using magnitudes
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simple turn detection by integrating over the gyro
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feeds the transition
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The smartphone's IMU is used to track the number of steps the pedestrian has made
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(accelerometer) as well as the relative heading change (gyroscope) since the last transition
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\cite{ebner-15}.%
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%
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To avoid potential sample impoverishment, which is induced when using the state transition as proposal distribution,
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we use both values directly within the transition step to constrain the to-be-walked distance and direction for the
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random walk.
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@@ -1,14 +1,18 @@
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\subsection{\docWIFI}
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absolute positioning using wifi (x,y,z)
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no fingerprinting
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uses signal-strength prediction model
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position of access points must be known beforehand
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only 2 parameters for all APs
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only vague position estimation
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the smartphone's \docWIFI{} component provides an absolute location estimation $(x,y,z)^T$ by
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measuring the signal-strengths of nearby transmitters (\docAP{}) and comparing them with the signal-strengths
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that should be measurable. The latter are determined using a signal-strength prediction model.
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Thus, no fingerprinting is required. Solely the positions of the \docAP{}s must be known beforehand.%
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%
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Prediction uses the wall-attenuation-factor model \cite{ebner-15}
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which needs just three parameters:
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the senders transmission power, the attenuation based on the distance from the sender, and the attenuation by floors/ceilings.
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To reduce the setup-time, the same values can be used for all transmitters at the expense of a worse location estimation / higher
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uncertainties.
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\subsection{\docIBeacon{}s}
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optional
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same as wifi
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but only one param: as txp is broadcasted
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If the building is equipped with \docIBeacon{}s, those may additionally be used as absolute location source.
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Like \docWIFI{}, measurements are compared with a model prediction based on the known transmitter position.
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The transmission power of the \docIBeacon{} is transmitted by the beacon itself and does not need any estimation beforehand.
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BIN
competition/tex/gfx/editor1.png
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PATH=$PATH:/mnt/data/texlive/bin/x86_64-linux/
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latex bare_conf.tex
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pdflatex bare_conf.tex
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bibtex bare_conf
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latex bare_conf.tex
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latex bare_conf.tex
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pdflatex bare_conf.tex
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pdflatex bare_conf.tex
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dvips bare_conf.dvi
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ps2pdf14 bare_conf.ps
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#dvips bare_conf.dvi
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#ps2pdf14 bare_conf.ps
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atril bare_conf.pdf&
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