AOA Based Localization and orientation estimation of UWB via Weighted Least Squares

In the Internet of Things (IoT), robot localization using ultra-wideband (UWB) technology requires estimating both the position and orientation of the robot. However, current localization and orientation estimation methods often rely on rigid body models, which require multiple anchors, leading to high system costs. To address this limitation, this paper focuses a self-localization approach based on the angle of arrival (AOA), which reduces complexity with only a single anchor. We propose an enhanced algorithm that employs weighted least squares (WLS) to improve estimation accuracy. Additionally, we derive the closed-form Cramér-Rao lower bound (CRLB) as a theoretical benchmark to evaluate the performance of the proposed algorithm. Both simulation results and real-world experiments demonstrate that the proposed algorithm achieves high localization accuracy, providing a cost-effective and efficient solution for IoT applications.