TWOR: Improving Modeling and Self-Localization in RFID-Tag Networks Under Colored Noise

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 positio...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.25583-25592
Main Authors: Ortega-Contreras, Jorge A., Andrade-Lucio, Jose A., Ibarra-Manzano, Oscar G., Shmaliy, Yuriy S.
Format: Article
Language:English
Subjects:
Autoregressive processes
colored noise
Distance measurement
Extended Kalman filter
extended unbiased FIR filter
Filtering algorithms
Finite impulse response filters
Impulse response
Kalman filters
Kinematics
Localization
Mathematical models
Noise measurement
Position measurement
Radio frequency identification
RFID tags
Robot kinematics
Robustness
self-localization
TWOR modeling
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3222397