AI/ML Assisted Design of Time-varying RF Metasurface for Interference Mitigation and Radar Jamming

AlchLight proposes to design, develop, and validate time-varying radio frequency metasurface (TV-RFM) for interference mitigation and radar jamming control to improve the resilience of fire control and tactical squad communication systems in congested electromagnetic (EM) environments Army aircraft body is generally made of a lightweight insulating material such as carbon fiber reinforced polymer. For RF functionality and surface charge mitigation, aircraft body is generally painted with multilayer dielectric coatings and ends with a subwavelength thin metal coating on the top. The coatings add substantial weight to the aircraft body. Therefore, it is essential to design a coating strategy and two-dimensional sub-wavelength structures to obtain the desired radio frequency response with minimal extra weight. In this project, we will use a machine learning model to optimize coatings and structures for the proposed TV-RFM. AlchLight will achieve the project goal by implementing the following two tasks; 1. Developing and testing a machine learning model for TV-RFM development; 2. ML assisted design and optimization of thin film coating for the proposed TV-RFM.