A Computational Approach based on a Particle Swarm Optimizer for Microwave Imaging of Two-Dimensional Dielectric Scatterers

A computational approach based on an innovative stochastic algorithm, namely the Particle Swarm Optimizer (PSO), is proposed for the solution of the inverse scattering problem arising in microwave imaging applications. The original inverse scattering problem is reformulated in a global nonlinear optimization one by defining a suitable cost function, which is minimized through a customized PSO. In such a framework, the paper is aimed at assessing the effectiveness of the proposed approach in locating, shaping, and reconstructing the dielectric parameters of unknown two-dimensional scatterers. Such an analysis is carried out by comparing the performance of the PSO-based approach with others state-of-the-art methods (deterministic as well as stochastic) in terms of retrieval accuracy as well as from a computational point of view. Moreover, an integrated strategy (based on the combination of the PSO and the iterative multi-scaling method (IMM) is proposed and analyzed to fully exploit complementary advantages of nonlinear optimization techniques and multi-resolution approaches. Selected numerical experiments, concerning dielectric scatterers different in shape, dimension, and dielectric profile, are performed starting from synthetic as well as experimental inverse scattering data.