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This paper describes a way to improve the indoor navigation of mobile robots using radio frequency identification (RFID) technology. A net of RFID tags is deployed in the navigation space. A measurement system measures distances from the tags to the robot with in the presence of the firstorder Markov-Gauss colored measurement noise (CMN) and is combined with a digital gyroscope to measure the robot heading. To increase the localization accuracy, the Kalman filter (KF) and unbiased finite impulse response (UFIR) modified for CMN are used. It is shown that the navigation system developed is more accurate than the basic one employing the standard KF and UFIR filter

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This work presents an object tracking process using Kalman and UFIR filters, standard and modified for Colored Measurement Noise (CMN). UFIR CMN showed favorable results with no ideal conditions, and KF CMN for ideal conditions.

This paper discusses the three-wheeled omnidirectional robot (TWOR) self-localization in radio frequency identification (RFID) tag environments. The nonlinear TWOR model is significantly improved by using geometric interpretation and incremental time representation in discrete time. The TWOR position and heading are self-estimated using distance measurements to RFID tags and a digital gyroscope in the presence of typical colored measurement noise (CMN). The extended unbiased finite impulse response (EFIR) is developed along with the extended Kalman filter (EKF) and their versions, cEKF and cEFIR, modified for Gauss-Markov CMN. A particle filter is used as a benchmark. It is shown that the cEFIR filter is more robust than the cEKF and almost as robust as the particle filter, although the latter is less accurate in real time.INDEX TERMS TWOR modeling, self-localization, colored noise, extended unbiased FIR filter, extended Kalman filter.