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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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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}