Environment Reconstruction in Terahertz Monostatic Sensing: Joint Millimeter-level Geometry Mapping and Material Identification

Terahertz (THz) integrated sensing and communication (ISAC) offers high-speed communication alongside precise environmental sensing. This paper presents a computationally efficient framework for THz-based environment reconstruction by integrating connected component analysis (CCA)-assisted multipath component (MPC) estimation with a sliding-window refinement strategy. To start with, a monostatic sensing experiment is conducted in an indoor scenario using a vector network analyzer (VNA)-based sounder operating from 290 to 310 GHz. On one hand, as for geometry mapping, a CCA-based region search is employed to accelerate parameter extraction, significantly reducing the search space for space-alternating generalized expectation-maximization (SAGE)-based estimation and achieving an 8.4 times acceleration, while preserving resolution. Further analysis of the connected component structure enables the identification of indoor features such as flat walls and corners. A sliding-window refinement applied to the identified regions improves geometric mapping, achieving the mean distance error of 4.9 mm, which is one order of magnitude better than the literature. On the other hand, the deterministic and stochastic components of the monostatic channel are classified through reflection loss analysis. Then, material identification is performed by looking up the reflection loss in a THz time-domain spectroscopy (THz-TDS) database, which comprises over 200 materials across a 0-6 THz range. Experimental results validate millimeter-level accuracy in geometry mapping and reliable material classification, enhancing the environmental awareness capabilities of THz ISAC systems.