Wi-Fi Indoor Localization Based on Hybrid Hypothesis Test of Signal Distribution

Wi-Fi indoor localization technique is one of the current research hotspots in the field of mobile computing, however, the conventional location fingerprinting based localization scheme does not consider the diversity of Wi-Fi signal distribution in the complicated indoor environment, resulting in the low robustness of indoor localization system. To address this problem, a new hybrid hypothesis test of signal distribution for Wi-Fi indoor localization is proposed. Specifically, the Jarque-Bera (JB) test is conducted to examine the normality of Wi-Fi signal distribution at each Reference Point (RP). Then, according to the different Wi-Fi signal distributions, the hybrid Mann-Whitney U test and T test approaches are used to construct the set of matching reference points with the purpose of realizing the area localization. Finally, by calculating the K-Nearest Neighbor (KNN) of matching reference points in the located area, the location coordinate of the target is obtained. The experimental results indicate that the proposed approach is featured with higher localization accuracy as well as stronger system robustness compared with the conventional Wi-Fi indoor localization approaches.