This thesis focuses on the development of microwave near-field sensing techniques for applications in biomedicine, agronomy, and geophysics. The primary objective is to design low-complexity algorithms that effectively solve complex inverse problems related to the characterization and detection of dielectric properties with various geometric distributions in heterogeneous media.
The candidate will begin by conducting a comprehensive review of existing radar-based and advanced signal processing methods. A precise physical model of microwave propagation in near-field conditions will be developed, serving as the foundation for new detection methods based on the concept of physics-driven iterative tomography. The ultimate goal is to formulate efficient algorithms suitable for real-time applications and validate them through experimental implementation. To achieve this, an evolving prototype setup will be developed, progressing from 2D media to more complex 3D scenarios.
This interdisciplinary project combines physical modeling, algorithm development, and practical experimentation. It presents an opportunity to advance the field of microwave imaging, with significant implications for biomedical and environmental applications.