Range-extended radar sensing using flying intelligent surfaces

• Applicants: Professor Dr.-Ing. Martin Mönnigmann; Professor Dr.-Ing. Aydin Sezgin
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics ; Communication Technology and Networks, High-Frequency Technology and Photonic Systems, Signal Processing and Machine Learning for Information Technology
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 541021107

The projects develops a new approach for extending the range and for increasing the accuracy of radar-based object tracking on the ground. For this purpose, uncrewed aerial vehicles (UAV) are equipped with reconfigurable intelligent surfaces (RIS). In a basic application, RIS reflect the radar signal, enabling redirection around obstacles. Beyond mere redirection, RIS can alter the signal phase in a controlled fashion, thus permitting the radar system to virtually reposition itself and to capture ground scenes from various perspectives. RIS are ideal for use with UAV due to their low weight, fast reaction times, and low power consumption. Even when equipped with state-of-the-art flight controllers, UAV are very susceptible to fluctuations in position and orientation, primarily caused by aerodynamic disturbances. The mirror-like redirection at the RIS significantly amplifies such disturbances. Consequently, even minor fluctuations in UAV pose are challenging, and improvements in pose control alone are unlikely to remedy this issue. Therefore, the phase shifting capability of the RIS is combined with beamsteering approaches to optimize the channel quality by mitigating UAV pose fluctuations, ultimately reducing the measurement uncertainty of radar tracking with UAV-mounted RISs. RISs are a young technology. Very few investigations of RISs so far encompass experimental research and validation. Existing experimental analyses consider isolated, stationary setups in radio laboratory settings. Theoretical and practically sound investigations of complex setups with RISs, such as the UAV-mounted RISs used here, have only recently emerged. The project will establish a physics- and data-based system model for UAV-mounted RISs. This model serves for both, uncertainty modeling, and uncertainty minimization by real-time phase control. Experimental data required for model and model uncertainty parametrization will be collected in a recently established flight laboratory, where aerodynamic disturbances and large pose estimation errors can be avoided. Subsequently, measurements during hovering flights in outdoor field experiments are carried out. Finally, the approach is applied to outdoor trajectory flights. In order to unfold their full potential, RISs must be configured dynamically, for example to compensate for the fluctuations in UAV pose in real-time. This involves demanding numerical optimization problems that must be solved in real-time. The project devotes considerable effort to developing optimization-based control algorithms for use in embedded computing on the UAV and in networked setups that combine embedded with base station calculations. The proficiency of the approach will be tested on the radar tracking of objects on the ground. The goal of the project is to demonstrate real-time RIS control can improve the channel quality of radar measurements, and to establish quantitative models and bounds for their uncertainty.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2433: Metrology on flying platforms