21 lines
2.3 KiB
TeX
21 lines
2.3 KiB
TeX
\abstract{
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Within this work we present an updated version of our award-winning indoor localization system for smartphones.
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The current position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models.
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Absolute positioning information is given by a comparison between recent \docWIFI{} measurements of nearby access points and signal strength predictions.
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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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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.
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Continuous and smooth floor changes are enabled by using a simple activity recognition.
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Our rapid computation scheme of the kernel density estimation allows to find an exact estimation of the pedestrian's current position.
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We further tackle advanced problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures, leading to a more robust localization.
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\newline
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The goal of this work is to propose a fast to deploy and low-cost localization solution, that
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provides reasonable results in a high variety of situations.
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To stress our system, we have chosen a very challenging test scenario.
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All experiments were conducted within a 13th century historic building, formerly a convent and today a museum.
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The system is evaluated using 28 distinct measurement series on four different test walks, up to \SI{310}{\meter} length and \SI{10}{\minute} duration.
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It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements.
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Our advanced filtering methods allow for a real fail-safe system, while the novel optimization scheme enables a setup-time of under \SI{120}{\minute} for the complete building.
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%We are able to resolve sample impoverishment whenever it occurs and thus provide a real fail-safe system.
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%finally compare the standard weighted-average estimator with our kernel density approach.
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}
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