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typos/wording in abstract

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FrankE
2018-09-17 18:17:48 +02:00
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tex/chapters/abstract.tex
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@@ -1,9 +1,9 @@
\abstract{
Within this work we present an updated version of our award-winning indoor localization system for smartphones.
The current position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models.
Absolute positioning information is given by a comparison between recent Wi-Fi 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 reference measurements to estimate a corresponding Wi-Fi model.
To model the pedestrian's movement, which is constraint by walls and other obstacles, we propose a state transition based upon navigation meshes, modelling only the buildings walkable areas.
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.
To model the pedestrian's movement, which is constrained by walls and other obstacles, we propose a state transition based upon navigation meshes, modeling only the building's walkable areas.
Continuous and smooth floor changes are enabled by using a simple activity recognition.
Our rapid computation scheme of the kernel density estimation allows to find an exact estimation of the pedestrian's current position.
We further tackle advanced problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures, leading to a more robust localization.