Distributed Variational Bayesian-Assisted Unscented Kalman Filter for Human Localization Under Indoor Environments

Herein, the distributed variational Bayesian-assisted unscented Kalman filter (VB-UKF) is used to enhance the accuracy of pedestrian positioning. For this method, a distributed filter is employed. First, the UKF under colored measurement noise (CMN) is derived. The VB-assisted method is then derived. Subsequently, the Mahalanobis distance is used to determine whether the current noise estimate conformed to the navigation environment; if the Mahalanobis distance is higher than the preset threshold, the VB-assisted method is employed to update the noise, which can improve the UKF accuracy under CMN. The effectiveness of the proposed method was subsequently validated through two practical tests. Experimental results demonstrate that this method plays a notable role in reducing localization errors in the experiments. This observation emphasizes the efficacy of the proposed method.