In this work we compare the performance of two well known filters for nonlinear models, the Extended Kalman Filter and the Unscented Kalman Filter, in estimating the position and orientation of a mobile robot. The two filters are driven by measurements taken by ultrasonic sensors that are located onboard the robot, and a switching sensors activation policy is devised, which allows power saving and accurate tracking. The experimental results show a substantial equivalence of the two filters, although in principle the approximating properties of the UKF are much better.