AoA-Based Iterative Positioning of IoT Sensors With Anchor Selection in NLOS Environments

Iterative positioning based on Angle-of-Arrival (AoA) measurements is currently arising as a solution to the positioning of Internet-of-Things (IoT) sensors in an indoor environment. We recently showed that iterating between the AoA and position estimation steps allows significant positioning gains. However, the existing algorithms only perform well under a Line-of-Sight (LOS) condition. In this letter, we propose an enhanced AoA-based iterative positioning algorithm with anchor selection in the presence of Non-Line-of-Sight (NLOS) propagation. The proposed algorithm can identify and mitigate the NLOS anchors by comparing the variances of the intermediate estimated position for all possible combinations of anchors with predefined thresholds. Finally, the estimated position based on the selected anchors is converted back to the angle information and used as prior information for the next iteration. The numerical results show that applying the anchor selection strategy significantly improves the positioning accuracy in indoor environments.