An improved WiFi indoor localization method combining channel state information and received signal strength

WiFi indoor localization has attracted much attention owing to the pervasive penetration of wireless local area networks (WLANs) and WiFi enabled mobile devices. Traditional WiFi indoor localization systems rely on received signal strength (RSS), which is instability and low space distinguish ability. Recently, channel state information (CSI) has been adopted instead of RSS and proven to be an efficient method. However, CSI raw data is sensitive to environment change. In this paper, we propose an improved WiFi indoor localization system combining CSI and RSS. Firstly, this study applies a time domain filtering method to reduce CSI data dynamic range. Then, we use the coherence bandwidth for CSI dimensional reduction. Afterwards, we extract a robust positioning feature from CSI and RSS. Finally, an improved weighted k-nearest neighbor algorithm based on kernel methods is used to estimate the location. The experiments demonstrate the effectiveness of the proposed system.