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toni
2018-09-18 15:35:05 +02:00
parent 4134f949b3
commit 235aaa5839
6 changed files with 9 additions and 11 deletions
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4
tex/chapters/estimation.tex
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@@ -25,8 +25,8 @@ In the case of particle filters the MMSE estimate equals to the weighted-average
\begin{equation}
\hat{\mStateVec}_t := \frac{1}{W_t} \sum_{i=1}^{N} w^i_t \mStateVec^i_t \, \text{,}
\end{equation}
\commentByMarkus{Passt die Notation so?}
\commentByFrank{sieht fuer mich auf den ersten blick nach korrektem weighted average aller partikel aus. was stoert dich?}
%\commentByMarkus{Passt die Notation so?}
%\commentByFrank{sieht fuer mich auf den ersten blick nach korrektem weighted average aller partikel aus. was stoert dich?}
where $W_t=\sum_{i=1}^{N}w^i_t$ is the sum of all weights.
While producing an overall good result in many situations, it fails when the posterior is multimodal.
In these situations the weighted-average estimate will find the estimate somewhere between the modes.

6
tex/chapters/eval.tex
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@@ -183,7 +183,7 @@ The larger the window, the slower changes become noticeable and vice versa.
Of course, the above suggested values are dependent upon the particular requirements and used sensors.
However, they should be valid for many modern commercially available smartphones.
\commentByFrank{hier hast du quotes um die activitites. in der intro noch nicht. vlt einheitlich machen ueber macros?}
%\commentByFrank{hier hast du quotes um die activitites. in der intro noch nicht. vlt einheitlich machen ueber macros?}
The activity is now evaluated using $p(\vec{o}_t \mid \vec{q}_t)_\text{act}$ by providing a probability based on whether the 3D location $\mPosVec$ of the state-in-question is on a staircase, in an elevator or on the floor.
If the current activity $\mObsActivity$ is recognized as "standing", a $\mPosVec$ located on the floor results in a probability given by $\kappa$, otherwise $1 - \kappa$.
The same applies to "walking up" and "walking down", here a $\mPosVec$ located on one of the possible staircases or elevators provides $\kappa$ and those who remain on the floor $1 - \kappa$.
@@ -193,7 +193,7 @@ In most cases, $\kappa = 0.75$ provides good results by remaining enough room fo
A significant higher value like $\kappa = 0.99$ could cause the system to be stuck on a staircase or to be unable to change floors.
\commentByToni{Wir haben im related work schon von particeln gesprochen. hier in der eval nehm ich aber wieder viel state und state-in-question. wie wollen wir es machen?}
%\commentByToni{Wir haben im related work schon von particeln gesprochen. hier in der eval nehm ich aber wieder viel state und state-in-question. wie wollen wir es machen?}
\commentByToni{warum wir die große treeppe so schwer ist: wlan model zieht JEDE decke ab, nicht nur die sichtbaren, weil das model einfach so gebaut wurde. }
%\commentByToni{warum wir die große treeppe so schwer ist: wlan model zieht JEDE decke ab, nicht nur die sichtbaren, weil das model einfach so gebaut wurde. }

2
tex/chapters/experiments.tex
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@@ -323,8 +323,6 @@ Ironically, this is again some type of sample impoverishment, caused by the afor
\subsection{Estimation}
\label{sec:eval:est}
\todo{boxkde 0.2 point2(1,1);}
As mentioned before, the single estimation methods (cf. chapter \ref{sec:estimation}) only vary by a few centimetres in the overall localization error.
That means, they differ mainly in the representation of the estimated locations.
More easily spoken, in which way the estimated path is drawn and thus presented to the user.

2
tex/chapters/introduction.tex
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@@ -44,4 +44,4 @@ The goal of this work is to propose a fast to deploy and low-cost localization s
Consequently, we believe that by utilizing our localization approach to such a challenging scenario, it is possible to prove those characteristics.
Finally, it should be mentioned that the here presented work is an highly updated version of the winner of the smartphone-based competition at IPIN 2016 \cite{Ebner-15}.
\blfootnote{Dankesagung und so weiter.}
\blfootnote{We would like to take this opportunity to thank Dr. Helmuth M\"ohring and all other employees of the Reichsstadtmuseum Rothenburg for the great cooperation and the provision of their infrastructure and resources. }

4
tex/chapters/misc.tex
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@@ -98,8 +98,8 @@ The quality factor is defined by
\noindent where $\bar\mRssi_\text{wifi}$ is the average of all signal strength measurements received from the observation $\mObsVec_t^{\mRssiVec_\text{wifi}}$. An upper and lower bound is given by $l_\text{max}$ and $l_\text{min}$.
The quality factor is extensively discussed within \cite{Ebner-17} and \cite{Fetzer-17}.
\commentByMarkus{Nochmal eine second method, meintest du third? wenn nicht versteh ich den Satz hier oder oben nicht}
Finally, we have all necessary tools to introduce a second method to prevent impoverishment.
%\commentByMarkus{Nochmal eine second method, meintest du third? wenn nicht versteh ich den Satz hier oder oben nicht}
Finally, we have all necessary tools to implement the second method to prevent impoverishment into the particle filter.
For this, the state transition model is extended.
Compared to the resampling step, as used by the first method, the transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$ enables us to use prior measurements, which is obviously necessary for all \docWIFI{} related calculations.
As described in chapter \ref{sec:transition}, our transition method only allows to sample particles at positions, that are actual feasible for a humans within a building e.g. no walking trough walls.

2
tex/chapters/system.tex
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@@ -36,4 +36,4 @@ The observation vector is defined as
\end{equation}
%
Here, $\mRssiVec_\text{wifi}$ contains the signal strength measurements of all \docAP{}s currently visible to the phone. $\mObsHeading$ provides the relative angular change and $\mObsSteps$ the number of steps since the last filter-step.
The result of a simple activity recognition using the phone's barometer and acceleromter is given by $\mObsActivity$, which is one of: standing, walking, walking up or walking down.
The result of a simple activity recognition using the phone's barometer and acceleromter is given by $\mObsActivity$, which is one of: "standing", "walking", "walking up" or "walking down".