Advancing Subsurface Radar: Simulation-to-Reality Gap Bridged with Deep Learning

This research leverages deep adversarial learning to improve subsurface radar sensing, specifically focusing on domain adaptation to bridge the gap between simulated data and real-world observations. The approach uses physics-guided hierarchical methods, indicating a potentially robust and interpretable solution for challenging environmental sensing tasks.

• •Applies deep adversarial learning for domain adaptation in subsurface radar.
• •Employs physics-guided hierarchical methods for enhanced robustness.
• •Aims to improve the accuracy and reliability of subsurface sensing.