Subspace-Based Optimization Method for Solving Inverse-Scattering Problems

This paper investigates a modified version of the subspace-based optimization method for solving inverse-scattering problems. The method is found to share several properties with the contrast-source-inversion method. The essence of the subspace-based optimization method is that part of the contrast source is determined from the spectrum analysis without using any optimization, whereas the rest is determined by optimization method. This feature significantly speeds up the convergence of the algorithm. There is a great flexibility in partitioning the space of induced current into two orthogonal complementary subspaces: the signal subspace and the noise subspace. This flexibility enables the algorithm to perform robustly against noise. Numerical simulations validate the efficacy of the proposed method: fast convergent and robust against noise.