Influence of contour smoothness and electric size on the profile reconstruction of metallic objects using hybrid optimization

Abstract

The main purpose of this article is to analyse the influence of the smoothness and electric size on the reconstruction of a metallic object contour in a 2D case. A multistage hybrid method based on Evolutionary Algorithms (EA) and an Interior-Point algorithm is used for the profile reconstruction. The purpose of this approach is to reduce the required amount of information as well as improving the convergence capabilities, specially when the object has a large electric size. The inverse problem is recast as a nonlinear optimization problem minimizing the difference between the measured and the estimated scattered field on an observation domain. The reconstruction of scatterers with different shapes and sizes is carried out to verify the accuracy of the method. In addition, two different EA are examined: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Numerical results show that a high reconstruction accuracy can be achieved, even when the Signal to Noise Ratio (SNR) is low.