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Small fixes abstract & intro

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Markus Bullmann
2018-10-16 17:10:52 +02:00
parent 49577037ff
commit 6c3604da16
2 changed files with 4 additions and 4 deletions
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tex_review/chapters/abstract.tex
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@@ -1,7 +1,7 @@
\abstract{
Within this work we present an updated version of our \del{award-winning} indoor localization system for smartphones.
The \add{pedestrian's} position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models.
Our \del{rapid computation} \add{recently presented approximation} scheme of the kernel density estimation allows to find an exact estimation of the current position\add{, instead of classical methods like weighted-average}.
Our \del{rapid computation} \add{recently presented approximation} scheme of the kernel density estimation allows to find an exact estimation of the current position\add{, compared to classical methods like weighted-average}.
%
Absolute positioning information is given by a comparison between recent \docWIFI{} measurements of nearby access points and signal strength predictions.
Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access points, we use an optimization scheme based on a few reference measurements to estimate a corresponding \docWIFI{} model.

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tex_review/chapters/introduction.tex
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@@ -28,7 +28,7 @@ Many unknown quantities, like the walls definitive material or thickness, make i
Additionally, \del{most wireless} \add{many of these} approaches are based on a line-of-sight assumption.
Thus, the performance will be even more limited due to the irregularly shaped spatial structure of such buildings.
Our approach tries to avoid those problems using an optimization scheme for Wi-Fi based on a \del{few} \add{set of} reference measurements.
We distribute a \del{small number} \add{set} of \del{simple} \add{small (\SI{2.8}{\centi\meter} x \SI{3.5}{\centi\meter})} and cheap \add{($\approx \SI{10}{\$}$)} \docWIFI{} beacons over the whole building \add{to ensure a reasonable coverage} and instead of measuring their position \add{and necessary parameters, we use our optimization scheme, initially presented in \cite{Ebner-17}}.
We distribute a \del{small number} \add{set} of \del{simple} \add{small (\SI{2.8}{\centi\meter} x \SI{3.5}{\centi\meter})} and cheap \add{($\approx \$10$)} \docWIFI{} beacons over the whole building \add{to ensure a reasonable coverage} and instead of measuring their position \add{and necessary parameters, we use our optimization scheme, initially presented in \cite{Ebner-17}}.
\add{An optimization scheme is able to compensate for wrongly measured access point positions, inaccurate building plans or other knowledge necessary for the Wi-Fi component.
}
@@ -69,7 +69,7 @@ The existing Wi-Fi infrastructure can consist of the aforementioned Wi-Fi beacon
The combination of both technologies is feasible, depending on the scenario and building.
Nevertheless, the museum considered in this work has no Wi-Fi infrastructure at all, not even a single access point.
Thus, we distributed a set of \SI{42}{beacons} throughout the complete building by simply plugging them into available power outlets.
Despite evaluating the novel contributions and the overall performance of the system, we have carried out additional experiments to determine the performance of our Wi-Fi optimization in such a complex scenario as well as a detailed comparison between KDE-based and weighted-average position estimation.}
In addition to evaluating the novel contributions and the overall performance of the system, we have carried out further experiments to determine the performance of our Wi-Fi optimization in such a complex scenario as well as a detailed comparison between KDE-based and weighted-average position estimation.}
%novel experiments to previous methods due to the complex scenario blah und blub.}
%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}.