Extended Kalman/UFIR Filters for UWB-Based Indoor Robot Localization Under Time-Varying Colored Measurement Noise

• Published in: IEEE internet of things journal Vol. 10; no. 17; pp. 15632 - 15641
• Main Authors: Xu, Yuan, Shmaliy, Yuriy S., Bi, Shuhui, Chen, Xiyuan, Zhuang, Yuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Colored measurement noise; Extended Kalman filter; extended Kalman filter (EKF); extended UFIR (EFIR) filter; Filter banks; Global Positioning System; Impulse response; Internet of Things; Localization; Location awareness; Noise measurement; Robot kinematics; Robot localization; Robots; Time measurement; ultra-wideband (UWB) localization; Ultrawideband
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3264980

In indoor robot localization by using ultra-wideband (UWB), the extended Kalman filter (EKF)-based algorithms suffer from the colored measurement noise (CMN) that degrades the localization accuracy and causes the divergence. To overcome this issue, we develop a hybrid colored EKF and colored extended unbiased finite impulse response (EFIR) filter (cEKF/EFIR filter) employing measurement differences. We also develop this algorithm using a filter bank on merged averaging horizons to be adaptive to time-varying CMN and call it the adaptive EKF/EFIR (aEKF/EFIR) filter. Experimental testing is provided in UWB-based indoor mobile robot localization environments. It is shown that the end-to-end colored EKF/EFIR and aEKF/EFIR filtering algorithms have better performances than the EKF, EFIR filter, and their modifications for CMN.