Abstract: The positioning technology based on channel state information (CSI) has been widely concerned in indoor scene applications. In order to improve the indoor positioning accuracy and stability of the WiFi signal multipath effect on the received signal strength indicator, this paper proposes a passive indoor fingerprint positioning algorithm based on CSI signal.  In the offline phase, the location is divided into blocks of the same size. The original data are filtered by the adaptive Kalman filter algorithm with variance compensation at each connection point, and then the filtered data are classified by the bisecting K-means clustering algorithm. The amplitude and phase information of the processed CSI are used as fingerprints. In the online stage, the real-time data collected by the test points are matched with the fingerprint database, and the located target does not need to carry any equipment. The simulation and field experiments show that the proposed algorithm can effectively reduce the multi-path attenuation effect of the signal receiver by using the sub-carrier characteristics in CSI signals, the positioning accuracy is significantly improved.

Key words: indoor localization, fingerprint localization, feature fingerprint, channel state information, bisecting K-means clustering algorithm