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FrankE
2017-05-05 14:33:13 +02:00
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tex/chapters/introduction.tex
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@@ -9,9 +9,9 @@
like step- and turn-detection. However, this requires the pedestrian's
initial position to be well known, e.g. using a GPS-fix just before
entering the building. Additionally, the sensor's error will sum up over
time.
time \cite{Koeping14}.
Depending on the used fusion-method, the latter can be addressed,
Depending on the used fusion-method, the 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.
@@ -24,25 +24,25 @@
it can be stabilized by the IMU and vice versa.
The downside of such an approach: both, \docWIFI{} and \docIBeacon{}s, require additional prior
knowledge to work: To infer the probability of the pedestrian currently
residing at an arbitrary location, one compares the signal strengths received
by the smartphone with the signal strengths one should receive at this
The downside of this approach is that both, \docWIFI{} and \docIBeacon{}s, require additional prior
knowledge to work. To infer the probability of the pedestrian currently
residing at an arbitrary location, the signal strengths received
by the smartphone are compared with the signal strengths which should be received at this
location (prior knowledge). As RF-signals are highly dependent
on the surroundings, those values can change rapidly within meters.
%
That is why fingerprinting became popular, where the required prior knowledge
is gathered by manually scanning each location within the building e.g.
using cells with \SI{2}{\meter} in size. While this provides the highest
possible accuracy due to actual measurements of the real situation,
one can easily realize the necessary amount of work for both, the initial
setup and maintenance when transmitters are changed or renovations take
place.
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
place, is very high.
To prevent setup- and maintenance effort, models can be used to predict
the signal strengths one should receive at some arbitrary location.
Depending on the used model, only a few parameters and the location of the
transmitter within the building are required. For newer installations
Setup- and maintenance effort can be prevented by using models to predict
the signal strengths that should be received at some arbitrary location.
Depending on the used model, only a few parameters and the locations of the
transmitters within the building are required. For newer installations
the latter is often available and tagged within the building's floorplan.
%As signals are attenuated by the buildings architecture like walls and floors,
%advanced models additionally include the floorplan within their prediction.
@@ -51,7 +51,7 @@
Furthermore, the choice of the model's parameters depends on the actual architecture and \docWIFI{} setup:
Parameters that work within building A might not work out within building B.
Thus, a compromise comes to mind: Instead of using hundreds of fingerprints,
Thus, a compromise comes to mind: Instead of using several hundreds of fingerprints,
a few reference measurements used for a model setup might be a valid tradeoff
between resulting accuracy and necessary setup time.