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

Abstract

In indoor robot localization by using Ultra-Wide Band (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 aEKF/EFIR filter. Experimental testing is provided in UWB-based indoor mobile robot localization environments. It is shown that the end-to-end cEKF/EFIR and aEKF/EFIR filtering algorithms have better performances than the EKF, EFIR filter, and their modifications for CMN.