Assessing EKF-based Orientation Uncertainties and its Impact on the Channels of UAV-mounted RIS

Reconfigurable intelligent surfaces (RIS) are a technology foreseen to play an important role in sixth-generation (6G) wireless networks. By manipulating and reflecting electromagnetic waves using adjustable reflecting elements, RIS can actively reshape the wireless environment in real time. This capability proves most effective when the RIS is positioned in line-of-sight and proximity to the transmitter and receiver due to multiplicative path loss. To meet these criteria in real-world applications, the RIS is mounted on an unmanned aerial vehicle (UAV). Combining UAV with RIS is particularly advantageous due to the lightweight and energy-efficient nature of the RIS. However, a UAV is exposed to many disturbances during flight, which can compromise the performance benefits of the RIS-enabled link. We show the UAV and RIS orientation can be estimated with an extended Kalman filter (EKF). By conducting experimental measurements and recording the UAV-mounted RIS orientation during flight, we quantify the uncertainties regarding the orientation estimation of the UAV and show their impact on the performance of the RIS link through numerical simulations.