Bayesian-Based Indoor Factory Positioning Using AOA, TDOA, and Hybrid Measurements

This work proposes and assesses Bayesian-based localization methods using time difference of arrival, angle of arrival, and hybrid measurements. First, a Bayesian localization model is constructed, and the Markov chain Monte Carlo method approximates the target’s 3D posterior distribution. Then, the model’s performance is evaluated in a 3GPP indoor factory environment using a distributed antenna system with a centralized controller for synchronizing and merging information. The received signal strength is used to select a subset of available anchors, and the estimation accuracy is measured in terms of horizontal and vertical errors. The results show that the Bayesian framework meets the horizontal and vertical errors requirements of the 3GPP for commercial cases with over 100 measurements. The accuracy can be improved by acquiring more measurements or increasing the number of active remote radio heads. However, when information fusion is applied (hybrid model), increasing the number of active anchors decreases the estimation performance.