THESIS
2021
1 online resource (x, 41 pages) : illustrations (some color)
Abstract
We consider pervasive localization where a user may sample widely different signal
modes (GPS,WiFi, geomagnetism, BLE, etc.) and values over time and space. Various
localization algorithms have already been proposed for different signal modes. To
achieve higher accuracy and pervasive localization, these signal modes may be fused.
Previous works in the area are often meticulously customized for only a few (two or
three) specific modes, and cannot support dynamic mode combination arisen from heterogeneous
sensor sampling rates and operating conditions. Some other recent works
assume a rather static environment or certain user behavior, and do not extend well
to environmental changes characterized by missing signal values, signal noise, device
heterogeneity, arbitrary phone carriage states...[
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We consider pervasive localization where a user may sample widely different signal
modes (GPS,WiFi, geomagnetism, BLE, etc.) and values over time and space. Various
localization algorithms have already been proposed for different signal modes. To
achieve higher accuracy and pervasive localization, these signal modes may be fused.
Previous works in the area are often meticulously customized for only a few (two or
three) specific modes, and cannot support dynamic mode combination arisen from heterogeneous
sensor sampling rates and operating conditions. Some other recent works
assume a rather static environment or certain user behavior, and do not extend well
to environmental changes characterized by missing signal values, signal noise, device
heterogeneity, arbitrary phone carriage states, etc.
We propose SiFu, a novel, highly accurate and generic multi-modal signal fusion
platform supporting arbitrary addition and combination of signal modes, and robust
against operational deviations from the original design point. To achieve genericity,
SiFu leverages upon any existing single-modal localization algorithms as black boxes,
and unifies them into a multi-modal likelihood framework. It employs Bayesian deep
learning to achieve high accuracy, and data augmentation to withstand against environmental variations. Using a weighted likelihood, it fuses the modes with inertial
sensor measurements by means of a particle filter. SiFu is simple to implement, and is
extensible to any emerging or future signals with only incremental training. We conduct
extensive experiments in three markedly different and representative sites (campus,
mall and subway station), and show that SiFu achieves significantly higher accuracy
as compared to other state-of-the-art approaches, cutting the localization error
by more than 20% in our experiments. It is also robust against environmental variations
(with 30% error reduction), even when signal values are greatly deviated from its
original designed settings.
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