Robust Wi-Fi indoor localization with KPCA feature extraction of dual band signals

Indoor localization system based Wi-Fi received signal strength (RSS) has gained popularity in recent years, as wireless local area networks and Wi-Fi enabled mobile devices are pervasive penetration. Unfortunately, the Wi-Fi RSS measurements are susceptible by device heterogeneity, multipath and signal noise, etc. To remedy these problems, we propose a robust Wi-Fi fingerprint-based indoor localization system. The proposed algorithm extract a robust positioning feature from Wi-Fi signals in both 2.4 GHz band and 5 GHz band by kernel principal component analysis (KPCA). Furthermore, we utilize Wi-Fi signal selection algorithm and coarse localization scheme for increasing localization accuracy and reducing the computational burden. Finally, the weighted k nearest neighbor method (WKNN) is used to obtain the estimated location. The proposed system implemented in a realistic indoor Wi-Fi environment, and results indicate that it is efficient in improving the positioning performance.