Wireless indoor positioning using online machine learning
Date
2019
Authors
Huang, S.
Ashfahani, A.
Pratama, M.
Editors
Wani, M.
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Conference paper
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Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019, 2019 / Wani, M. (ed./s), pp.1885-1890
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18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 (16 Dec 2019 - 19 Dec 2019 : Florida, US)
Abstract
The indoor positioning system can be applied to smart factories to monitor the location of time-critical items in real-time. It is useful for planning and control in the dynamic manufacturing environment. The challenge of the localization is the non-stationary characteristics of the environment. In this paper, we present a predictive modeling method using online machine learning. The wireless signals from time-critical items can be captured constantly. The online positioning model is built and updated by using the sensor data stream.
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Copyright 2019 IEEE