Multi-Resolution Subspace-Based Optimization Method for the Retrieval of 2D Perfect Electric Conductors

TL;DR

This paper introduces a multi-resolution subspace-based optimization method for accurately reconstructing 2D perfect electric conductors from scattered field data without prior information, demonstrating improved robustness and efficiency.

Contribution

The paper develops an IMSA-SOM inversion technique that combines multi-scaling and subspace optimization for PEC imaging without needing prior scatterer details.

Findings

Validated against synthetic and experimental data

Demonstrated robustness to noise

Showed improved computational efficiency

Abstract

Perfect Electric Conductors (PECs) are imaged integrating the subspace-based optimizationmethod (SOM) within the iterative multi-scaling scheme (IMSA). Without a-priori information on the number or/and the locations of the scatterers and modelling their EM scattering interactions with a (known) probing source in terms of surface electric field integral equations, a segment-based representation of PECs is retrieved from the scattered field samples. The proposed IMSA-SOM inversion method is validated against both synthetic and experimental data by assessing the reconstruction accuracy, the robustness to the noise, and the computational efficiency with some comparisons, as well.