An Improved Indoor Localization of WiFiBased on Support Vector Machines

Indoor localization based on existing WiFi signal strength is becoming increasingly prevalent and ubiquitous. The user-based localization algorithm utilizes the information of the Received Signal Strength(RSS) from the surrounding access points(APs) to determine the user position. In this paper, focusing on the development of a user localization uses existing WiFi environment for its low cost and ease of deployment. We propose an indoor localization of WiFi based on support vector machines(ILW-SVM), and use the bilinear median interpolation method(BMIM) to reduce the calibration effort on creating fingerprint map while still retaining the accuracy of user localization. According to comparison of accuracy of three different kernel functions, choosing the radial basis function(RBF) as kernel function. In addition, we also propose improved ILW-SVM algorithm to solve the indoor localization that nearest neighbor points are not concentrated. At last, overall comparison of kNN, ILW-SVM and improved ILW-SVM in consideration of accuracy. Experimental results indicate that the proposed algorithm can effectively reduce the calibration effort and exhibit superior performance in terms of localization accuracy and stabilization.