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Microwave Profiling of Multiple Buried Scatterers with Arbitrary Curvilinear Shapes

The quantitative microwave profiling of buried dielectric objects with arbitrary curvilinear cross-sections, is addressed in this paper. As the problem is highly non-linear, the Covariance Matrix Adoption Evolution Strategy (CMA-ES), as a global stochastic optimization algorithm is implemented. Having the limited amount of measured data in these scenarios, it is very vital to reduce the number of problem unknowns, especially for modelling the curvilinear shapes. Here, the cubic-spline expansion is utilized to model the scatterer shape, using few numbers of unknowns. Moreover, an initialization step using diffraction tomography method is used to approximately detect the permittivity values and locations of scatterers and as a result reduce the search domain. The numerical results show that the proposed algorithm can acceptably retrieve two closely located high-contrast objects, using fewer number of unknowns, compared to previously reported partitioned search space techniques.