Localization of mobile users using trajectory matching

We present an algorithm enabling localization of moving wireless devices in an indoor setting. The method uses only RF signal strength and can be implemented without specialized hardware. The mobility of the users is modeled by learning a function mapping a short history of signal strength values to a 2D position. We use radial basis function (RBF) fitting to learn a reliable estimate of a mobile node's position given its past signal strength measurements. Even though we deal with extremely noisy measurements in a cluttered indoor setting, nodes are not required to be stationary during measurement or learning. We evaluate our algorithm in a real indoor setting using MicaZ motes, achieving an average localization accuracy of 1.3 m. In our experiments, using historical data improves the localization accuracy by almost a factor of two compared to using only the most current measurements.