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Self-Localization Over RFID Tag Grid Excess Channels Using Extended Filtering Techniques

Accuracy of target self-localization in RFID tag information networks and grids critically affects its situation awareness. With an insufficient localization accuracy, information about local 2D or 3D surroundings delivered to a target by request may provoke collisions, even fatal. In RFID networking systems, target state can be observed over a big number of tags. For such a case, the extended Kalman filter (EKF) algorithm is modified and a new extended unbiased finite impulse response (EFIR) filtering algorithm is developed. We show that redundant information captured from the tags allows increasing both the localization accuracy and system stability. The common factor here is that the number of tags required to increase accuracy is limited in the target nonlinear medium, by about six in our case. It is also shown that target state observation over the RFID tag excess channels allows mitigating effect of the imprecisely defined noise statistics on the EKF performance and preventing divergence in EKF.