A probabilistic fingerprinting method for indoor localization based on RBF network

Received Signal Strength Indication (RSSI) fingerprinting is known as the most concerned method for indoor localization as its high accuracy and low cost. Numerous RSSI based methods have shown an attractive performance but the major drawback is the high dependency on the database construction. In this paper, we propose a localization method based on radial basis function (RBF) network. Choosing Gaussian radial basis functions with appropriate widths, the probability algorithm can be effectively conducted to the RBF network regardless of deficiency of the RSSI data. By further conducting the supervised learning of RBF network the RM database can be calibrated and updated once some new dataset is available, so as to achieve a better localization performance. Experimental results in a multi-floors building verify that the performance of the proposed RBF network is superior to other common used methods.