The development of applications for mobile autonomous systems to carry out a determined task requires as basic assumption the knowledge of the current position of the mobile system. To fulfill this necessity, a lot of different methods have been proposed, some more accurate than others. Therefore, when there are methods to calculate a position estimation under different conditions and using different hardware, the key is to find an estimation as accurate as possible. Using a position estimation coming from one of this methods, it is possible to apply an Extended Kalman filtering to make the estimation better. This position introduced in the EKF does not have to be so accurate and could depend on the conditions and the available hardware. Moreover, if a group of robots which are able to communicate among them are sharing the same environment, it is possible to improve the accuracy of the position estimation using data coming from the other robots and the relative distances to these robots measured by some sensor. This is the basic principle of the method developed in this master thesis.