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FrankE
2017-05-06 21:33:58 +02:00
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tex/chapters/introduction.tex
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@@ -11,7 +11,7 @@
entering the building. Additionally, the sensor's error will sum up over
time \cite{Koeping14}.
Depending on the used fusion-method, the latter can be addressed
Depending on the used fusion-method, latter can be addressed
using a movement model for the pedestrian, that prevents unlikely movements
and locations. However, this will obviously work only to some extent and still
requires the initial position to be at least vaguely known.
@@ -36,7 +36,7 @@
using cells of $\approx \SI{2}{\meter}$ in size. This usually leads to
a very high accuracy due to actual measurements of the real situation.
However, the amount of work for the initial
setup and the maintenance when transmitters are changed or renovations take
setup and the maintenance, when transmitters are changed or renovations take
place, is very high.
Setup- and maintenance effort can be prevented by using models to predict
@@ -67,7 +67,7 @@
just empirical parameters (no setup time when transmitter positions are known) to optimized
parameters, where no prior knowledge is necessary and a few reference measurements suffice.
Despite analyzing the \docWIFI{} performance on its own, we will also have
Besides analyzing the \docWIFI{} performance on its own, we will also have
a closer look at the resulting performance-changes within a fully featured smartphone-based
indoor localization system using a movement model based on the building's floorplan,
together with various other sensors and recursive state estimation based on a particle filter.