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added some comments. more to-do

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FrankE
2018-09-17 19:31:03 +02:00
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tex/chapters/introduction.tex
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@@ -22,20 +22,21 @@ We also use a novel approach for finding an exact estimation of the pedestrian's
Many historical buildings, especially bigger ones like castles, monasteries or churches, are built of massive stone walls and have annexes from different historical periods out of different construction materials.
This leads to problems for methods using received signal strengths (RSS) from \docWIFI{} or Bluetooth, due to a high signal attenuation between different rooms.
Many unknown quantities like the walls definitive material or thickness make it expensive to determine important parameters, \eg{} the signal's depletion over distance.
Many unknown quantities, like the walls definitive material or thickness, make it expensive to determine important parameters, \eg{} the signal's depletion over distance.
Additionally, most wireless 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.
We distribute a small number of simple and cheap \docWIFI{} beacons over the whole building and instead of measuring their position, we use an optimization scheme based on some reference measurements.
We distribute a small number of simple and cheap \docWIFI{} beacons over the whole building and instead of measuring their position, we use an optimization scheme based on a few reference measurements.
An optimization scheme also avoids inaccuracies like wrongly positioned access points or fingerprints caused by outdated or inaccurate building plans.
\commentByFrank{warum fingerprints? das verwirrt mich an der stelle. willst du sagen, dass opt. besser ist, als ueberhaupt fingerprints zu nehmen? dann kommt es nicht so rueber. unsicher, deshalb kein direkter fix sondern comment}
It is obvious, that this could be solved by re-measuring the building, however this is a very time-consuming process requiring specialized hardware and a surveying engineer.
Clearly, this is contrary to most costumers expectations of a fast to deploy and low-cost solution.
In addition, this is not only a question of costs incurred, but also for buildings under monumental protection, what does not allow for larger construction measures.
In addition, this is not only a question of costs incurred, but also for buildings under monumental protection, not allowing for larger construction measures.
To sum up, this work presents a smartphone-based localization system using a particle filter to incorporate different probabilistic models.
We omit time-consuming approaches like classic fingerprinting or measuring the exact positions of access-points.
Instead we use a simple optimization scheme based on reference measurements to estimate a corresponding Wi-Fi model.
Instead we use a simple optimization scheme based on reference measurements to estimate a corresponding \docWIFI{} model.
The pedestrian's movement is modeled realistically using a navigation mesh, based on the building's floorplan.
A barometer based activity recognition enables going into the third dimension and problems occurring from multimodalities and impoverishment are taken into account.