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Online Offline Learning for Sound-based Indoor Localization Using Low-cost Hardware

Authors:
Machhamer, R. ,  Dziubany, M. ,  Czenkusch, L. ,  Laux, H. ,  Schmeink, A. ,  Gollmer, K.-U. ,  Naumann, S. ,  Dartmann, G.
Journal:
IEEE Access
Volume:
7
Page(s):
155088-155106
Date:
Oct. 2019
ISSN:
2169-3536
DOI:
10.1109/ACCESS.2019.2947581
hsb:
RWTH-2020-00048
Language:
English

Abstract

Online Learning algorithms and Indoor Positioning Systems are complex applications in theenvironment of cyber-physical systems. These distributed systems are created by networking intelligentmachines and autonomous robots on the Internet of Things using embedded systems that enable the exchangeof information at any time. This information is processed by Machine Learning algorithms to make decisionsabout current developments in production or to influence logistics processes for optimization purposes.In this article, we present and categorize the further development of the prototype of a novel IndoorPositioning System, which constantly adapts its knowledge to the conditions of its environment with thehelp of Online Learning. Here, we apply Online Learning algorithms in the field of sound-based indoorlocalization with low-cost hardware and demonstrate the improvement of the system over its predecessorand its adaptability for different applications in an experimental case study.

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