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

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.

Automated summary

In this paper, a UWB-based indoor robot localization technique is proposed. The hybrid colored EKF and colored EFIR filter algorithm is developed to overcome the issues caused by CMN, and measurement differences are utilized. Experimental results show that the proposed algorithm performs better than traditional algorithms in handling CMN.