Learned Global Optimization for Inverse Scattering Problems: Matching Global Search With Computational Efficiency

The computationally efficient solution of fully nonlinear microwave inverse scattering problems (ISPs) is addressed. An innovative system-by-design (SbD)-based method is proposed to enable, for the first time to the best of the authors’ knowledge, an effective, robust, and time-efficient exploitation of an evolutionary algorithm (EA) to perform the global minimization of the data-mismatch cost function. According to the SbD paradigm as suitably applied to ISPs, the proposed approach is found on: 1) a smart reformulation of the ISP based on the
a priori
information on the imaged targets for defining a minimum dimensionality and representative set of degrees of freedom (DoFs) and 2) the artificial intelligence (AI)-driven integration of a customized global search technique with a digital twin (DT) predictor based on the Gaussian process (GP) theory. Representative numerical and experimental results are provided to assess the effectiveness and the efficiency of the proposed approach also in comparison with competitive state-of-the-art inversion techniques.