A NONLINEAR FILTERING APPROACH FOR SMARTPHONE-BASED INDOOR NAVIGATION

This paper considers the problem of indoor navigation by means of low cost mobile devices. The required accuracy, the low reliability of low-cost sensor measurements and the typical unavailability of the GPS signal, make indoor navigation a challenging problem. In this paper a Bayesian probabilistic approach is presented in order to obtain good navigation performance in indoor environment: the proposed method is based on the integration of information provided by the inertial navigation system measurements, the radio signal strength of a standard wireless network, and of geometrical information of the building. The resulting algorithm is based on multiple hypothesis tracking and nonlinear optimization. Sensor measurements are also used to estimate some specific characteristics of the environment. The navigation accuracy achievable with the proposed method is evaluated by means of a set of tests carried out in a university building.