Abstract
Indoor location-based mobile applications have been gaining momentum in reshaping the daily activities of Internet users. A large number of indoor localization techniques achieve the localization goal by analyzing the received signal strength indication (RSSI) of pervasive WiFi signals. Compared with RSSI, the channel state information (CSI) provides more comprehensive time and space information with more complex hardware and software cost. In this paper, we proposed two CSI-based indoor localization algorithms: 1) a localization algorithm based on the weighted linear discriminant analysis; 2) a localization algorithm based on two-dimensional principal component analysis. The experimental results show that the proposed algorithms outperform the basic Bayesian algorithm based on the principal component analysis on improving the localization accuracy and reducing the computational complexity.
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