An Approach to Model Lunar Regolith With HFSS for Synthetic SAR Data Generation

Synthetic aperture radar (SAR) provides critical insights into planetary surfaces, but obtaining real SAR data for lunar exploration is fraught with challenges, including high costs and limited sensor configurations. To overcome these constraints, this study leverages electromagnetic simulations to generate synthetic SAR data that accurately represents the scattering behavior of lunar regolith. Using HFSS, we model the complex interactions of microwave signals with lunar surface elements, varying parameters such as incident angles, frequencies, and polarizations. The resulting synthetic data serves as a robust resource for training and validating machine learning models, enhancing the interpretation of SAR imagery for lunar surface analysis. This work underscores the potential of synthetic data in advancing lunar exploration technologies and supports the development of more effective SAR-based applications for future missions.