Continuous location and direction estimation with multiple sensors using particle filtering

In this paper we discuss the use of particle filtering to estimate the values of several state variables describing a user's context. Since particle filtering algorithms are computationally efficient realizations of Bayesian filters they perform exceptionally well to optimally combine the a priori knowledge stemming from behavioral models, such as movement models, and the noisy measurements from sensors. The estimate at each time step is obtained in the form of a probability density function that represents the entire information and quantifies the inherent uncertainty about the context. The concept has been realized in simulations and experiments. In this paper, the applied movement model is presented with simulated measurements from GPS and compass sensors to illustrate the concept