Radar On-Site Calibration and Scattering Analysis by Physics-Defined Machine Learning

In microwave sensing applications such as ground-penetrating radar (GPR), a central objective is to determine whether the measured scattering response originates from surface clutter or subsurface structures. This task is challenging because the observed signals are highly sensitive to measurement conditions, propagation effects, and environmental variability, making direct interpretation based on fixed reference signatures unreliable. Conventional approaches either rely on physics-based inversion, which is often ill-posed and computationally demanding, or employ data-driven learning models that may achieve strong empirical performance but offer limited physical interpretability.

In this work, we propose a physically grounded framework for scattering analysis based on a structured representation of the scattering process. Specifically, a morphological scattering spectrum is introduced as a latent representation defined over frequency and angular domains, whose structure is derived from the underlying physics of wave propagation. Rather than learning representations directly from data, the proposed approach formulates the problem as the estimation and tracking of this physically defined state. In this interpretation, machine learning is used to infer the occupancy and statistical organization of the latent representation, while the representation geometry itself remains constrained by the admissible scattering physics.

For practical applications, the estimated morphological scattering spectrum provides a compact and interpretable description of the dominant scattering mechanisms present in the observations. Surface and subsurface responses are expected to exhibit distinct occupancy patterns and structural characteristics within this representation, enabling robust discrimination between ground and underground targets. By combining physically defined representations with data-driven statistical inference, the proposed framework offers a principled approach for classification and interpretation in complex scattering environments.