Reconstruction of two-dimensional buried objects by a differential evolution method

Abstract

The application of a global optimization procedure to the detection of buried inhomogeneities is studied in the present paper. The electromagnetic inverse problem is recast as a global optimization problem and iteratively solved by a differential evolution method. The approach is applied to two-dimensional configurations. The paper provides the mathematical formulation of the method together with a discussion on the application of the stochastic optimization procedure. Finally, the results of some numerical simulations are reported.

Exploitation of Te-Tm Scattering Data for Microwave Imaging Through the Multi-Scaling Reconstruction Strategy

“…Although very effective global optimizers are available (e.g., Genetic Algorithms (GAs) [34][35][36], Particle Swarm Optimizer (P SO) [37][38][39][40], Differential Evolution (DE) [41,42], Memetic Algorithms (M A) [43,44], a deterministic approach is used to focus on the "effect" of data on the reconstructions thus avoiding the randomness of stochastic method. The multi-step procedure terminates at s = S opt when the stability conditions, defined in [31] for the T M case, hold true.…”

Electromagnetic Inverse Scattering of Axially Moving Cylindrical Targets

“…Microwave inverse scattering techniques [9] have been proved to possess excellent properties in the reconstruction of permittivity profiles in breast cancer detection [10], [11], ground penetrating radar applications [12]- [17], crack detection [18], and so on. It seems, however, that such techniques have not been used so far for the solution of inverse scattering problems involving axially moving cylinders, when the reconstruction of the velocity profile is of interest.…”

Electromagnetic imaging within the contrast-source formulation by means of the multiscaling inexact Newton method

“…Such problems arise when imaging buried objects [7][8][9][10][11][12][13][14] and biological tissues [15][16][17][18][19][20][21][22][23][24][25]. Because of the difficulty of the problem at hand, generally nonlinear and ill posed [1], great efforts have been addressed over the years to develop suitable and reliable solution techniques both deterministic [26][27][28][29] and stochastic [30][31][32][33][34][35] as well as qualitative [36][37][38][39][40][41][42][43][44][45][46][47] and quantitative [48][49][50][51][52][53][54]…”