Inversion of a layered rough surface model: maximizing the number of retrievable parameters for the design of future subsurface sensing radar systems

We previously applied an optimization technique known as Simulated Annealing (SA) to the inverse problem associated with a 3D two-layer dielectric structure with slightly rough interfaces and showed that simulated annealing methods are capable of globally minimizing cost functions with many local minima [1]. Nonlinearity of the cost function is a major factor that decreases the performance of the inversion algorithm. With a fixed set of measurement and inversion parameters, as the number of unknown model parameters increases, the cost function nonlinearity becomes more severe, decreasing the efficiency of inversion and making the annealing process perform like an inefficient brute force search. The focus of this work is on strategies to choose the optimal set of measurement parameters for retrieval of the largest possible number of parameters of a layered dielectric structure.