Adaptive Distributed Student's T Extended Kalman Filter Employing Allan Variance for UWB Localization.

This study proposes an adaptive distributed Student's t extended Kalman filter (EKF) using Allan variance for ultrawide-band (UWB) localization. First of all, we model the state equation using the target's position and velocity in east and north directions and the measurement equation by using distance between the UWB base station (BS) and the target object. Then, the adaptive distributed filter employs a federation structure: A local t EKF is designed to estimate the target's position by fusing the distance between the UWB base station and the target object. The main filter fuses the local filter's outputs and computes the final output. For the local t EKF, in order to overcome the problem that noise in the Kalman method is assumed to be white noise and difficult to adapt to practical application environments, the t distribution is used to model noise. Meanwhile, Allan variance is calculated to assist the local filter, which improves the adaptive ability. Experimental results show that the proposed method effectively enhances navigation accuracy compared to the distributed EKF.