Anchor Selection In Angle-Of-Arrival Estimation-Based Localization Using Polynomial Chaos Expansions

Angle-of-arrival (AoA) estimation-based localization systems are a promising technology in the context of Internet-of-Things. The system considered is a densely deployed set of anchors equipped with arrays of antennas able to measure the AoA of the signal emitted by the device to be located. Naturally, the AoA measurements are prone to uncertainties due to noise, channel conditions and uncertainties on the anchors placement and orientations. In this work, a method based on Polynomial Chaos Expansions is proposed to perform anchor selection in order to enhance the precision of the positioning. An experimental setup with six anchors is implemented to evaluate the performance of the proposed method. The experimental results demonstrate that by applying our anchor selection method, the performance of the localization system improves.