An Indoor Localization of WiFi Based on Support Vector Machines

The recent growing interest for indoor localization-based services has created a need for more accurate and real-time indoor localization solutions. Indoor localization based on existing WiFi signal strength is becoming increasingly prevalent and ubiquity. In this paper, we utilize the information of the signal strength received from the surrounding access points (APs) to determine the user localization. The propose algorithm based on support vector machines (SVM) algorithm, and comparing with three kernel functions, radial basis function (RBF) performs best of all. Experimental results indicate that the proposed algorithm leads to improvement on localization accuracy.