LAUSR

Most of the current indoor localization methods based on channel state information (CSI) utilize the amplitude and phase of each subcarrier individually as location fingerprints, but the correlation between adjacent subcarriers also contains essential location information. Therefore, this paper proposes a device-free indoor localization method based on CSI and limited penetrable horizontal visibility graph (LPHVG), which does not require the subject to carry a device and can be implemented with only a single access point (AP). Firstly, we model the frequency correlation between subcarriers by LPHVG algorithm and construct a CSI-based complex network. Secondly, the topology of the complex network is utilized to analyze the relationship between CSI adjacent subcarriers and extract network features, which are combined with statistical features as fingerprint information to characterize different locations. Finally, the method is combined with support vector regression (SVR) to realize indoor localization in different environments. The experimental results show that the proposed method can significantly improve the localization accuracy, and has an excellent performance in different indoor scenarios.