Subspace migration for imaging of thin, curve-like electromagnetic inhomogeneities without shape information
For practitioners of inverse scattering, this work removes the need for geometric priors in subspace migration, making it more broadly applicable, though the improvement is incremental.
The paper identifies the mathematical structure of subspace migration for imaging thin, curve-like electromagnetic inhomogeneities without requiring prior shape information, and improves the technique using multi-frequency data. Numerical simulations with noisy data confirm the effectiveness.
It is well-known that subspace migration is stable and effective non-iterative imaging technique in inverse scattering problem. But, for a proper application, geometric features of unknown targets must be considered beforehand. Without this consideration, one cannot retrieve good results via subspace migration. In this paper, we identify the mathematical structure of single- and multi-frequency subspace migration without any geometric consideration of unknown targets and explore its certain properties. This is based on the fact that elements of so-called Multi-Static Response (MSR) matrix can be represented as an asymptotic expansion formula. Furthermore, based on the examined structure, we improve subspace migration and consider the multi-frequency subspace migration. Various results of numerical simulation with noisy data support our investigation.