Wireless Indoor Positioning Using Online Machine Learning

Sheng Huang, Andri Ashfahani, Mahardhika Pratama

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.