Trainingless fingerprinting-based indoor positioning algorithms with smartphones using electromagnetic propagation models

Recent multimedia and location-based services (LBSs) employ information about location, orientation, and context of a mobile device. Moreover, the wide spread adoption of Smartphones, usually equipped with powerful processors, accelerometers, compasses, and Global and Hybrid Positioning Systems (GPS/HPSs) receivers, has favored the increasing of location- and context-based services over the last years. In this work a Wi-Fi fingerprint-based indoor positioning techniques is considered. It is aimed at supporting possible location aware services. The main novelty introduced in this paper concerns the training phase, which is usually needed by these techniques. In our approach, the training phase is avoided, since opportune simulative propagation models of the environment in which the algorithm are working are introduced. The resulting technique allows exploiting the accuracy of the fingerprinting approaches and, simultaneously, avoids the heavy training phase, which represents one of the main drawbacks of such techniques.