TY  - JOUR
A1  - Machhamer, Rüdiger
A1  - Dziubany, Matthias
A1  - Czenkusch, Levin
A1  - Laux, Hendrik
A1  - Schmeink, Anke
A1  - Gollmer, Klaus-Uwe
A1  - Naumann, Stefan
A1  - Dartmann, Guido
T1  - Online offline learning for sound-based indoor localization using low-cost hardware
T2  - IEEE Access
N2  - Online Learning algorithms and Indoor Positioning Systems are complex applications in the environment of cyber-physical systems. These distributed systems are created by networking intelligent machines and autonomous robots on the Internet of Things using embedded systems that enable the exchange of information at any time. This information is processed by Machine Learning algorithms to make decisions about 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 Indoor Positioning System, which constantly adapts its knowledge to the conditions of its environment with the help of Online Learning. Here, we apply Online Learning algorithms in the field of sound-based indoor localization with low-cost hardware and demonstrate the improvement of the system over its predecessor and its adaptability for different applications in an experimental case study.
KW  - Online-Algorithmus
KW  - Autonomer Roboter
KW  - Maschinelles Lernen
KW  - fingerprint recognition
KW  - incremental learning
KW  - indoor localization
KW  - internet of things
KW  - learning vector quantization
KW  - machine learning
KW  - online learning
KW  - signal processing
Y1  - 2019
UR  - https://hst.opus.hbz-nrw.de/frontdoor/index/index/docId/100
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:tr5-1008
VL  - 7
SP  - 155088
EP  - 155106
PB  - IEEE
ER  -