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semester or other student projects
Learning-based approaches for feature fusion in multi-modal indoor localization
Cabrini, Vincent
January 24, 2020
Due to their aptitude to capture complex dependencies, neural networks are a promising candidate for indoor localization. Omnipresent phenomena such as multi-path signal propagation, shadowing and device noise introduce non-linear effects in the data, and make conventional geometry-based methods fail even in simple environments. This semester project explores few analytical outlier rejection algorithms and new fusion methods based on neural networks and compares them with an analytical model.
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Name
Semester_Project_Vincent_Cabrini.pdf
Type
Publisher's version
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openaccess
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646.37 KB
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Adobe PDF
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