CSI-based autoencoder classification for Wi-Fi indoor localization

Recently, indoor localization problem has drawn a wide range of attention. However, there are few researches that can keep balance between accuracy and expense, and few plans can achieve both device-free and accuracy. To solve this problem, the scheme of CSI-based autoencoder classification for Wi-Fi indoor localization is proposed. Only one wireless router and one computer are placed as signal emitter and receiver respectively. With so few ordinary devices, expenses have been decreased to a large extent. Device-free is achieved by performing localization based on Wi-Fi signal. Channel State Information (CSI) is measured and calculated to decrease the multipath effect, which reaches a higher accuracy. With the use of CSI, a mass of data are obtained. Machine learning including autoencoder and BP network are utilized owing to their advantage of processing mass data. In our experiment, this plan achieves 2-dim localizing with an accuracy of 50 cm.