Indoor position estimation with CSI

Abstract

Due to recent outbreaks of infectious diseases and miscellaneous accidents, it has become important to understand the state of crowds. In this study, we used Channel State Information (CSI), which represents the transmission status of Wi-Fi radio waves, to estimate the location of people in a room by considering the amplitude of each subcarrier as a feature as a multi-level classification task in a random forest. In laboratory experiments, the accuracy was high when the training data include the data of the person whose location was to be estimated, but the accuracy deteriorated when the training data did not. In addition, the use of moving average of the series data improved the estimation accuracy for many combinations of data.