AGV indoor positioning algorithm based on ultra-wideband and MCKF

To address the degradation in accuracy and reliability of ultra-wideband (UWB)-based indoor localization for automated guided vehicles (AGVs) under non-line-of-sight (NLOS) conditions, this paper proposes an MCKF-enhanced Chan-Taylor fusion localization algorithm. First, the double-sided two-way ranging (DS-TWR) method is employed to obtain the distances between the tag and base stations. To mitigate NLOS-induced ranging errors, the maximum correntropy Kalman filter (MCKF) is introduced to process the measured data. Localization equations are then established based on the time difference of arrival (TDOA) principle. The Chan algorithm is used to generate the initial position estimate, which is further refined by the Taylor algorithm through iterative optimization. Experimental results show that, compared with the conventional Chan-Taylor algorithm, the proposed method improves localization accuracy by 1.95% and 56.57% in static and dynamic tests under line-of-sight (LOS) conditions, respectively. Under NLOS conditions, the accuracy is further improved by 40.22% and 32.98% in static and dynamic tests, respectively, compared with the KF+Chan-Taylor algorithm. These results demonstrate that the proposed algorithm effectively suppresses NLOS errors and enhances the accuracy and robustness of AGV indoor localization in complex environments