Ill-Posedness Analysis of CSI-Based Electromagnetic Inverse Scattering for Material Reconstruction in ISAC Systems

TL;DR

This paper analyzes the mathematical causes of ill-posedness in CSI-based electromagnetic inverse scattering for material reconstruction in ISAC systems, proposing methods to improve problem conditioning and robustness.

Contribution

It provides a detailed analysis of the electromagnetic inverse problem's operator structure and introduces an ROI-constrained QP framework to enhance stability and efficiency.

Findings

ROI restriction reduces condition number and improves stability

Simulation results show enhanced robustness and reduced complexity

Analysis explains the role of coherence in the scattering operator

Abstract

Channel state information (CSI)-based electromagnetic inverse scattering for material reconstruction in ISAC systems enables physics-grounded, material-aware DT. Yet the resulting CSI-induced scattering operator is often severely ill-conditioned. To understand the origin of the ill-posedness, this paper analyzes the mathematical properties of the electromagnetic inverse problem and investigates the operator structure of the ISAC scattering matrix jointly shaped by in-domain scattering responses and Tx/Rx propagation channels. We show that background-related matrix columns are highly coherent and dominate the near rank deficiency, whereas scatterer-related columns are comparatively weakly correlated; their coherence decreases with the number of probing frequencies and thus contributes to the effective rank. Motivated by this analysis, we prove that restricting the ROI around the true scatterer yields a provable condition-number reduction and a tightened CRLB, and we quantify the impact of ROI mismatch numerically. To operationalize these insights, an ROI-constrained QP framework is adopted, where a linear sampling method delineates a coarse ROI and the QP update is performed in the reduced subspace. Full-wave FDTD simulations over multiple geometries and SNR validate pronounced conditioning improvement, substantial complexity savings, and improved robustness, consistent with the proposed analysis, compared with the full-domain formulation.