Electromagnetic inverse scattering of two-dimensional perfectly conducting objects by real-coded genetic algorithm

Shape reconstruction of two-dimensional perfectly conducting objects using noisy measured scattering data is considered in this paper. The contour of each conducting object is denoted by a shape function in the local polar coordinate which is approximated by a trigonometric series. A point-matching method is used to solve the scattering problem. The main idea of the inversion algorithm is to cast the inverse problem into a restrained minimization problem and to solve it by the real-coded genetic algorithm (RGA). The performance of this algorithm is demonstrated by numerically reconstructing arbitrarily shaped objects and by a detailed comparison with both the standard genetic algorithm (SGA) and the Newton-Kantorovitch method.