ADAPTIVE DISTRIBUTED STUDENT’S T-BASED EXTENDED KALMAN FILTER USING ALLAN VARIANCE FOR UWB LOCALIZATION
Keywords:
UWB localization, Extended Kalman Filter, Sensor fusion, Allan variance.Abstract
For ultrawide-band (UWB) localization, this work suggests an adaptive distributed Student's t extended Kalman filter (EKF) using Allan variance. First, we model the measurement equation using the distance between the UWB base station (BS) and the target item, and the state equation using the target's position and velocity in the east and north directions. Next, the adaptive distributed filter uses a federation structure: By combining the distance between the target object and the UWB base station, a local EKF is intended to estimate the position of the target. The main filter computes the final result by combining the outputs of the local filter. The distribution is used to simulate noise for the local EKF in order to get around the issue that noise in the Kalman method is expected to be white noise and challenging to adapt to real-world application contexts. Allan variance is computed in the interim to help the local filter, which enhances adaptability. Compared to the distributed EKF, the experimental results demonstrate that the suggested technique significantly improves the accuracy of navigation.
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