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added Evalbase.h again

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toni
2016-05-05 10:35:59 +02:00
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6
tex/chapters/conclusion.tex
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@@ -1,3 +1,9 @@
\section{Conclusion}
map information into smoothing. better way and faster then just dijkstra. compensate big jumps caused by wifi. better method for estimation and drawing of particles in backward simulation. more advanced smoothing transition. not used evaluating using the observations, but using the given information for more advanced approaches.
\begin{figure}
\input{gfx/activity/activity_over_time}
\caption{activity recognition}
\label{fig:activityRecognition}
\end{figure}

26
tex/chapters/introduction.tex
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@@ -15,11 +15,11 @@ Additional improvements can be achieved by using environmental information about
In most cases, probabilistic methods are used to incorporate those highly different sensor types.
Here, a probability distribution describes the pedestrian's possible whereabouts and therefore the uncertainty of the system.
Drawing
\commentByLukas{Drawing samples oder sampling}
%\commentByLukas{Drawing samples oder sampling}
from a probability distribution and
\commentByLukas{Willst du hier das Samplen erwaehnen? Es kommt so ein bisschen aus dem Nichts und man kann es gerade nur schwer einordnen}
\commentByToni{Ich möchte hier auf Monte Carlo ueberleiten. Warum macht man das ueberhaupt? Ich finde das umschreibt das ganz gut. alles andere kostet nur unfassbar viel platz wie ich finde.}
\commentByFrank{vlt als Kompromiss einfach etwas umstellen/kuerzen: Describing/Modelling (multimodal) probability densities analytically is in most cases...}
%\commentByLukas{Willst du hier das Samplen erwaehnen? Es kommt so ein bisschen aus dem Nichts und man kann es gerade nur schwer einordnen}
%\commentByToni{Ich möchte hier auf Monte Carlo ueberleiten. Warum macht man das ueberhaupt? Ich finde das umschreibt das ganz gut. alles andere kostet nur unfassbar viel platz wie ich finde.}
%\commentByFrank{vlt als Kompromiss einfach etwas umstellen/kuerzen: Describing/Modelling (multimodal) probability densities analytically is in most cases...}
finding an analytical solution for densities is in most cases a difficult task, especially in case of time sequential, non-linear and non-Gaussian models.
Due to the high complexity of the human movement, we consider indoor localisation as such.
@@ -38,9 +38,9 @@ For example, a barometer can be used to determine the probability of being on a
Despite the many advances made in the last years, nearly all systems suffer from more or less the same problems.
Like mentioned before, PDR suffers from an accumulating bias,
the signal of Wi-Fi gets attenuated by walls
\commentByFrank{falls noch platz ist: noch mehr nachteile :P \docWIFI{} location estimation strongly depends on the quality of the signal-strength estimation model (oder fingerprinting) and the way the smartphone is held}
%\commentByFrank{falls noch platz ist: noch mehr nachteile :P \docWIFI{} location estimation strongly depends on the quality of the signal-strength estimation model (oder fingerprinting) and the way the smartphone is held}
and the barometric pressure is highly affected by weather patterns and humidity
\commentByFrank{spontane fenster/tuer oeffnung}
%\commentByFrank{spontane fenster/tuer oeffnung}
\cite{Binghao13-UBI}.
That is the reason for the use of statistical methods in the first place. Nevertheless, there are even more profound problems regarding the whole position estimation procedure.
@@ -53,7 +53,7 @@ A solution to recover from such filter divergences faster, involves methods to r
However, even this can not completely prevent delays.
Another reason for possible time delays are slow sensor updates.
For example, most mobile devices restrict the Wi-Fi module to update only every few seconds, to save on battery.
%
\begin{figure}[t]
\centering
\def\svgwidth{0.9\columnwidth}
@@ -64,12 +64,12 @@ For example, most mobile devices restrict the Wi-Fi module to update only every
The most likely position (green line) is estimated somewhere in-between. After a right turn, the distribution slowly starts to recover its unimodality.}
\label{fig:multimodalPath}
\end{figure}
%
Further critical problems arise from multimodal distributions.
Those are caused by multiple possible position estimates.
Fig. \ref{fig:multimodalPath} illustrates an example where a floor gets separated by a wall.
Due to inaccurate measurements and a PDR approach for evaluating the movement, the distribution splits apart.
Therefore, the most likely position \commentByFrank{wenn avg ueber alle particles, was ja default ist} is somewhere in-between.
Therefore, the weighted average position is somewhere in-between.
Only after the pedestrian turns right, the distribution is again unimodal, since moving through walls is impossible.
As one can imagine, this can lead to serious problems in big indoor environments.
Such a situation can be improved by incorporating future measurements (e.g. the right turn) or predictive information (e.g. the most likely path) to the filtering procedure \cite{Ebner-16}.
@@ -89,12 +89,10 @@ The main goal is to solve above mentioned problems and to investigate new possib
All of our contributions are supported by an extensive experimental evaluation.
\commentByLukas{In der Einleitung sollte an einer Stelle ganz klar die Contributions der Arbeit herausgestellt werden. Vielleicht irgendwie sowas:
The contributions of this work are as follows:
Firstly, we extend current smoothing methods for indoor localisation to resolve multimodalities during the state estimation process. Secondly, we incorporate the knowledge of the user's destination as a-priori knowledge. Thirdly, we enrich the state transition model with an activity recognition to distinguish between walking, standing and walking stairs. All of our contributions are supported by an extensive experimental evaluation. }
%\commentByLukas{In der Einleitung sollte an einer Stelle ganz klar die Contributions der Arbeit herausgestellt werden. Vielleicht irgendwie sowas: The contributions of this work are as follows:Firstly, we extend current smoothing methods for indoor localisation to resolve multimodalities during the state estimation process. Secondly, we incorporate the knowledge of the user's destination as a-priori knowledge. Thirdly, we enrich the state transition model with an activity recognition to distinguish between walking, standing and walking stairs. All of our contributions are supported by an extensive experimental evaluation. }
\commentByToni{Steht doch direkt einen Absatz drueber nur halt kein plakatives "The contributions of " steht. Nur das Smoothing ist die Contribution meiner Meinung nach. Das prior knowledge ist ausm fusion paper. lediglich die activity rec fehlte. habe es ergänzt :). da bin ich mir aber noch nicht sicher... es wird ja nicht wirklich evaluiert sondern eher als "gegeben" angesehen. vielleicht dann auch eher so beschreiben?}
\commentByFrank{finds gut so}
%\commentByToni{Steht doch direkt einen Absatz drueber nur halt kein plakatives "The contributions of " steht. Nur das Smoothing ist die Contribution meiner Meinung nach. Das prior knowledge ist ausm fusion paper. lediglich die activity rec fehlte. habe es ergänzt :). da bin ich mir aber noch nicht sicher... es wird ja nicht wirklich evaluiert sondern eher als "gegeben" angesehen. vielleicht dann auch eher so beschreiben?}
%\commentByFrank{finds gut so}

32
tex/chapters/relatedwork.tex
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@@ -21,7 +21,7 @@ On the other hand, fixed-interval smoothing requires all observations until time
The origin of MC smoothing can be traced back to Genshiro Kitagawa.
In his work \cite{kitagawa1996monte} he presented the simplest form of smoothing as an extension to the particle filter.
This algorithm is often called the filter-smoother since it runs online and a smoothing is provided while filtering.
\commentByFrank{das mit dem weighted paths irritiert mich etwas. war das original work auch fuer etwas, wo pfade im spiel waren? weils halt gar so gut passt. ned dass da begrifflichkeiten durcheinander kommen. beim lesen fehlt mir das beim 1. anlauf was damit gemeint ist}
%\commentByFrank{das mit dem weighted paths irritiert mich etwas. war das original work auch fuer etwas, wo pfade im spiel waren? weils halt gar so gut passt. ned dass da begrifflichkeiten durcheinander kommen. beim lesen fehlt mir das beim 1. anlauf was damit gemeint ist}
This approach uses the particle filter steps to update weighted paths $\{(\vec{X}_{1:t}^i , W^i_t)\}^N_{i=1}$, producing an accurate approximation of the filtering posterior $p(\vec{q}_{t} \mid \vec{o}_{1:t})$ with a computational complexity of only $\mathcal{O}(N)$.
However, it gives a poor representation of previous states due a monotonic decrease of distinct particles caused by resampling of each weighted path \cite{Doucet11:ATO}.
Based on this, more advanced methods like the forward-backward smoother \cite{doucet2000} and backward simulation \cite{Godsill04:MCS} were developed.
@@ -29,41 +29,39 @@ Both methods are running backwards in time to reweight a set of particles recurs
Algorithmic details will be shown in section \ref{sec:smoothing}.
%wo werden diese eingesetzt, paar beispiele. offline, online
\commentByFrank{wenn du meinst, 'bei indoor wirds NICHT verwendet' dann ist 'as' das falsche. wuerde auch 'got' statt 'gets' verwenden}
In recent years, smoothing gets attention mainly in other areas as indoor localisation.
%\commentByFrank{wenn du meinst, 'bei indoor wirds NICHT verwendet' dann ist 'as' das falsche. wuerde auch 'got' statt 'gets' verwenden}
In recent years, smoothing got attention mainly in other areas than indoor localisation.
The early work of \cite{isard1998smoothing} demonstrates the possibilities of smoothing for visual tracking.
They used a combination of the CONDENSATION particle filter with a forward-backward smoother.
Based on this pioneering approach, many different solutions for visual and multi-target tracking have been developed \cite{Perez2004}.
For example, in \cite{Platzer:2008} a particle smoother is used to reduce multimodalities in a blood flow simulation for human vessels. The authors of \cite{Hu2014} use a smoother to overcoming the problem of particle impoverishment while predicting the Remaining Useful Life (RUL) of equipment (e.g. a Lithium-ion battery).
%smoothing im bezug auf indoor
\commentByFrank{their -> there?}
Nevertheless, their are some promising approaches for indoor localisation systems as well.
Nevertheless, there are some promising approaches for indoor localisation systems as well.
For example \cite{Nurminen2014} deployed a fixed-interval forward-backward smoother to improve the position estimation for non-real-time applications.
They combined Wi-Fi, step and turn detection, a simple line-of-sight model for floor plan restrictions and the barometric change within a particle filter.
The state transition samples a new state based on the heading change, altitude change and a fixed step length.
The experiments of \cite{Nurminen2014} clearly emphasize the benefits of smoothing techniques. The estimation error could be decreased significantly.
\commentByFrank{treated? behandelt? examined? evaluated?}
However, a fixed-lag smoother was treated only in theory.
However, a fixed-lag smoother was discussed only in theory.
In the work of \cite{Paul2009} both fixed-interval and fixed-lag smoothing were presented.
They implemented Wi-Fi, binary infra-red motion sensors, binary foot-switches and a potential field for floor plan restrictions.
Those sensors were incorporated using a sigma-point Kalman filter in combination with a forward-backward smoother.
\commentByFrank{then -> than?}
It was also proven by \cite{Paul2009}, that the fixed-lag smoother is slightly less accurate then the fixed-interval smoother, as one would expect from the theoretical foundation.
It was also proven by \cite{Paul2009}, that the fixed-lag smoother is slightly less accurate than the fixed-interval smoother, as one would expect from the theoretical foundation.
Unfortunately, even a sigma-point Kalman filters is after all just a linearisation and therefore not as flexible and suited for the complex problem of indoor localisation as a non-linear estimator like a particle filter.
\commentByToni{Kann das jemand nochmal verifizieren? Das mit dem Kalman Filter. Danke.}
\commentByLukas{Ich wuerde den Satz ganz weglassen. Ansonsten musst du angeben, wo die eigentlichen Probleme liegen, also z.B. in welchen konkreten Situation das Kalman Filter nicht mehr funktioniert usw. So ist es jetzt erstmal nur eine Behauptung ohne jeglichen Hintergrund.}
\commentByToni{Ich bin mir nicht sicher ob das eine Behauptung ohne jeglichen Hintergrund ist. Meiner Meinung nach ist das ziemlich weitreichend bekannt. Finde den Satz persoenlich ganz gut, weil er uns deutlich von dieser Arbeit abgrenzt und das ist wichtig.}
\commentByFrank{hab zwar ka was das ist, aber vermutlich ist es auch normal-dist also unimodal? dann verweisen wir doch einfach auf fig1 mit dem zusatz: 'sowas geht garned erst'}.
%\commentByToni{Kann das jemand nochmal verifizieren? Das mit dem Kalman Filter. Danke.}
%\commentByLukas{Ich wuerde den Satz ganz weglassen. Ansonsten musst du angeben, wo die eigentlichen Probleme liegen, also z.B. in welchen konkreten Situation das Kalman Filter nicht mehr funktioniert usw. So ist es jetzt erstmal nur eine Behauptung ohne jeglichen Hintergrund.}
%\commentByToni{Ich bin mir nicht sicher ob das eine Behauptung ohne jeglichen Hintergrund ist. Meiner Meinung nach ist das ziemlich weitreichend bekannt. Finde den Satz persoenlich ganz gut, weil er uns deutlich von dieser Arbeit abgrenzt und das ist wichtig.}
%\commentByFrank{hab zwar ka was das ist, aber vermutlich ist es auch normal-dist also unimodal? dann verweisen wir doch einfach auf fig1 mit dem zusatz: 'sowas geht garned erst'}.
%\commentByToni{Sigma Point Kalman Filter können mit Multimodalitäten umgehen... auch wenn sie linearisieren. frag nicht wie das gehen soll.}
Additionally, the Wi-Fi RSSI model requires known calibration points and is deployed using a remarkable number of access points for very small spaces.
In our opinion this is not practical and does not suite real-world conditions.
Since humans with a specific destination in mind do not tend to change their directions randomly, we would further recommend adding a PDR-based transition to draw samples in a more directed manner instead of scattering them randomly in every direction.
\commentByFrank{algorithmS?}
\commentByFrank{'is able to use', oder 'will use'? gehts um die eval (will use), oder generell um die theorie und moeglichkeiten (is able to)}
\commentByFrank{man koennte die reihenfolge vlt umstellen, erst die ganzen filtering sachen beschreiben, map, activity, ... und on top of that two smoothing algorithms both implemented as fixed-interval and fixed-lag?}
The here presented approach is able to use two different smoothing algorithm, both implemented as fixed-interval and fixed-lag versions.
%\commentByFrank{algorithmS?}
%\commentByFrank{'is able to use', oder 'will use'? gehts um die eval (will use), oder generell um die theorie und moeglichkeiten (is able to)}
%\commentByFrank{man koennte die reihenfolge vlt umstellen, erst die ganzen filtering sachen beschreiben, map, activity, ... und on top of that two smoothing algorithms both implemented as fixed-interval and fixed-lag?}
The here presented approach will use two different smoothing algorithm, both implemented as fixed-interval and fixed-lag versions.
Further, our localisation system presented in \cite{Ebner-16} enables us to walk stairs and thus going into the third dimension.
Therefore, a regularly tessellated graph is utilized to avoid walls, detecting doors and recognizing stairs.
Within this work, this is additionally supported by a simple classification that detects the activities unknown, standing, walking and walking stairs.