THESIS
2017
xi, 42 pages : illustrations ; 30 cm
Abstract
Geomagnetic field holds much promise for indoor localization due to its pervasive spatial
presence, high signal stability and wide availability of magnetometers already embedded in
mobile devices. Previous work in the area often fuses it with a pedometer (step counter) via
particles. These approaches are computationally intensive and require strong assumptions on
user behavior. To overcome that, we propose Magil, a pedometer-free approach which makes
use of magnetic field for indoor localization. To the best of our knowledge, this is the first
piece of work using geomagnetism for smartphone localization without need of a pedometer.
Magil is applicable to any open or complex indoor environment. In the offline phase, Magil
continuously collects and stores geomagnetic fingerprints...[
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Geomagnetic field holds much promise for indoor localization due to its pervasive spatial
presence, high signal stability and wide availability of magnetometers already embedded in
mobile devices. Previous work in the area often fuses it with a pedometer (step counter) via
particles. These approaches are computationally intensive and require strong assumptions on
user behavior. To overcome that, we propose Magil, a pedometer-free approach which makes
use of magnetic field for indoor localization. To the best of our knowledge, this is the first
piece of work using geomagnetism for smartphone localization without need of a pedometer.
Magil is applicable to any open or complex indoor environment. In the offline phase, Magil
continuously collects and stores geomagnetic fingerprints while a surveyor is walking in the
indoor area. In the online phase, it employs a fast algorithm to match the geomagnetic
segments whose fingerprint variations best match the target observations. Given the closely
matched segments, Magil constructs the user path efficiently with a modified shortest path
formulation by selecting and connecting these matched segments, hence obtaining the target
locations over time.
To further increase localization performance, we propose MagFi, which extends Magil by
fusing it with Wi-Fi signals. In the offline phase, MagFi further collects opportunistic Wi-Fi
RSSI for RSSI fingerprint construction. In the online phase, MagFi leverages RSSI to further
enhance the results. We have implemented both Magil and MagFi, and conducted extensive
experiments at our university campus. Our results show that both systems outperform state-of-the-art schemes by a wide margin (often cutting localization error by more than 30%).
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