Localization of Mobile Robots with RFID Technology and Expectation Maximization Algorithm

In this study, we proposed a new way to localize mobile robots in a very noisy environment. The mobile robot is equipped with an active RFID reader and some tags are placed in the room to provide RF Beacons in order that the robot can localize itself with the known tag geographical locations. The RFID equipments are working in 916 MHz band and the tags are battery enabled so the range of the experiment can effectively increase to 50 m. First there is a model estimated for the noise in the environment, which can be expressed as a Gaussians distribution then the RFID propagation model is obtained from a series of experimental tests. There are two different methods for noisy data filtering, Kalman Filtering as the best ever used method and a new method of particle filters with expectation maximization core. The diversity and multi-path effects in this experiment were considered as unwanted signal effects. The results show a good convergence in the EM method after very low iterations. The advantage of the EM method to Kalman filtering is not relying on the initial values. The precision of this new method in a normal environemt is between 4-7 cm in >10 iterations.