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wifi adjustments+comments

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k-a-z-u
2018-10-17 15:34:59 +02:00
parent 14b8fcddee
commit 957a95ad09
2 changed files with 15 additions and 7 deletions
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15
tex_review/chapters/eval.tex
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@@ -1,6 +1,8 @@
\section{Evaluation}
\label{sec:evaluation}
%\newcommand{\ourWifiModel}{log-distance + ceilings model}
The probability density of the state evaluation in \eqref{equ:bayesInt} is given by
%
\begin{equation}
@@ -46,9 +48,16 @@ The comparison between a single RSSI measurement $\mRssi_i$ and the reference is
\noindent where $\mu_{i,\mPosVec}$ denotes the (predicted) signal strength for the \docAPshort{} identified by $i$, regarding the location $\mPosVec$.
A certain noise is allowed by the corresponding standard deviation $\sigma_{\text{wifi}}$.
Within this work $\mu_{\mPosVec}$ is calculated by a modified version of the wall-attenuation-factor model as presented in \cite{Ebner-17}.
\add{We only consider floors and ceilings in order to avoid computation-intensive intersection-tests with every wall along the line-of-sight.
Especially for a building like the one discussed in this paper, this assumption is reasonable due to the complex and historically grown architecture as well as the many different wall materials to be determined.}
Within this work $\mu_{i,\mPosVec}$ is calculated by a compromise between the log-distance model and the
wall-attenuation factor model \cite{radar}, as presented in \cite{Ebner-17}.
\add{
The model only considers floors/ceilings, as including walls demands for costly intersection tests to determine all walls along the signal's line-of-sight.
While including walls within the model would increase the accuracy of the model's prediction \cite{PropagationModelling, radar},
for many use-cases it is sufficient to just consider floors/ceilings,
to reduce the performance impact when being used on smartphones.
%Especially for a building like the one discussed in this paper,
%this assumption is reasonable due to the complex and historically grown architecture as well as the many different wall materials to be determined.
}
Therefore, the prediction depends on the 3D distance $d$ between the \docAPshort{} in question and the location $\mPosVec$ as well as the number of floors $\Delta f$ between them:
\begin{equation}

7
tex_review/review/03_review.txt
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@@ -71,10 +71,9 @@ Ln 237: "...the average acceleration..." This includes both linear acceleration
determined the phone's current orientation, to undo the rotation, present within the gyroscope's readings.
Ln 258 - This equation needs revision. Should it be "p(s_i|p) ~ N(u_i,p , std²_wifi)" ? Also the wall-attenuation-factor-model only takes into account attenuation by floors, not walls.
-> TODO: Eigentlich passt das mit der NV, für Ihn tdz ändern? Und das model nimmt keine wände, weil wir keine wände nehmen :D.
@Frank hervorheben dass wir nicht WAF sondern log-dist-ceiling benutzen
walls wären problemlos möglich, allerdings kostet das dann viel zu viel zeit die schnittpunkte zu analysieren
-> No, the equation is correct. Its the actual >result< of the normal distribution when questioned for the received s_i, given the model prediction was u_i,p with uncertainty \sigma^2_wifi
-> We now made clear that our model is something in between the log-distance and the wall-attenuation factor model. To reduce computation time on the smartphone, only floors/ceilings are considered
as this can be achieved without costly intersection tests. We also pointed out, that including walls would be more accurate, but is costly during runtime (intersection-tests).
Ln 271-272: The authors mention that their WiFi fingerprinting approximation process is faster than classical fingerprinting, but they fail to provide a reference for an example of the latter or significant metrics such as the average time per square meter for fingerprinting a whole building. Furthermore, the authors should also take into account that while there are approaches where reference measurements are recorded on small grids between 1 to 2m, there are also approaches able to record reference measurements using faster methods. One example is walking by the building while registering ground truth points and using dead reckoning techniques (see Guimarães, V. et al. A motion tracking solution for indoor localization using smartphones. In Proceedings of the 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)).
-> TODO: vielleicht den satz hier entfernen und im related work darauf hinweisen, dass es auch andere schnelle ansätze gibt? Wobei wir im related work schon [20] gecited haben, der genau das macht! vielleicht erwähnen wir seinen noch, damit er zufrieden ist? Oder wir zeigen das kleine fingerprints schneller ist als laufen? was vermutlich nicht der fall ist.