Time Domain Image Reconstruction for Homogenous Dielectric Objects by Dynamic Differential Evolution

International audienceA transient acoustic wave multiparameter inverse problem is studied. The aim is to simultaneously retrieve three constitutive parameters and one geometrical parameter of a lossy fluid-like cylinder by nonlinear full-wave inversion of synthetic data. Contrary to most publications treating this subject, it is assumed herein that the retrieval model is not identical to the data model, this being so because some of the parameters (priors) in the retrieval model are different (by the simple fact of being more or less unknown) from their true counterparts in the data model. This so-called model discordance, which is the usual situation in real-world inverse problems, is a source of errors for the retrieval of the other parameters. Moreover, it is a source of non-uniqueness and instability, a fact revealed by the employment of a properly-tuned Nelder-Mead downhill Simplex optimization algorithm, and which requires a second-order regularization for its resolution. Retrieval errors as a function of prior uncertainty are computed, and found to be large, for various model discordance scenarios involving one or two uncertain priors, for data with and without noise

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“…[17,25]), and • enabling nonlinear full (transient)-wave inversions (e.g. [20,[26][27][28]) to be carried out in a reasonable amount of time.…”

Section: Introductionmentioning

confidence: 99%

Multiparameter identification of a lossy fluid-like object from its transient acoustic response

Scotti

Wirgin

2013

Inverse Problems in Science and Engineering

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“…These algorithms, which are based on stochastic strategies, offer advantages relative to local inversion algorithms, including strong search ability simplicity, robustness, and insensitivity to 452 C.-C. Chiu and C.-H. Sun ill-posedness. In contrast to traditional computation systems, evolutionary computation (Sun et al, 2010a(Sun et al, , 2010c(Sun et al, , 2008Rekanos, 2008;Semnani et al, 2009Semnani et al, , 2010Chien et al, 2009;Donelli et al, 2006;Chiu et al, 2009;Chien & Chiu, 2005) provides a more robust and efficient approach for solving inverse scattering problems. Particle swarm optimization (PSO) has proven to be a useful method of optimization for difficult and discontinuous multidimensional engineering problems (Poli et al, 2010).…”

Section: Introductionmentioning

confidence: 98%

Computational Approach Based on a Differential Evolution with Self-Adaptive Concept for Microwave Imaging of Two-Dimensional Inverse Scattering Problem

Chiu

Sun

2012

Electromagnetics

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The application of four techniques for the shape reconstruction of a metallic cylinder by measured scattered fields is studied in this article. These approaches are applied to two-dimensional configurations. Finite-difference time-domain is employed for the analysis of the forward scattering part, while the inverse scattering problems are transformed into optimization problems. Different differential evolutionary algorithms are applied to reconstruct the location and shape of the two-dimensional metallic cylinder. These techniques have been tested in the case of simulated measurements contaminated by additive white Gaussian noise. The reconstructed results by algorithm with self-adaptive control parameter settings are better than these obtained by the standard differential evolution algorithm and dynamic differential evolution algorithm.

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“…Moreover, it was shown that DDE outperforms traditional DE in terms of convergence speed. In the 2010, the dynamic differential evolution (DDE) was first proposed to deal with the shape reconstruction of homogeneous dielectric cylinders under time domain [21]. It is also found that DDE algorithms have good convergences compared with PSO methods in the inverse scattering problems [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…In general, they tend to get trapped in local minima when the initial trial solution is far from the exact one. Thus, some populationbased stochastic methods, such as genetic algorithm (GA) [6][7][8][9]18], differential evolution (DE) [11,[19][20][21] particle swarm optimization (PSO) [12,13,[22][23][24][25], are proposed to search the global extreme of the inverse problems to overcome the drawback of the deterministic methods.…”

Section: Introductionmentioning

confidence: 99%

Time Domain Inverse Scattering of Buried Inhomogeneous Dielectric Cylinders

Chiu

Chen

2012

J Nondestruct Eval

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This paper presents the studies of time domain inverse scattering for a two-dimensional inhomogeneous dielectric cylinder buried in a half-space by the finite difference time domain (FDTD) method and evolutionary algorithms (EAs). For forward scattering, the FDTD method is employed to calculate the scattered E fields, while for the inverse scattering the evolutionary algorithms are utilized to determine the permittivity of the buried cylindrical scatterer with arbitrary cross section. The results obtained for different examples show that the dynamic differential evolution (DDE) algorithms outperform the non-uniform steady state genetic algorithm (NU-SSGA) variants in terms of finding best optima. The suitability and efficiency of applying these two methods for microwave imaging of 2-D inhomogeneous dielectric cylinders are examined. Moreover, when the measured scattered fields are contaminated with Gaussian noise, DDE is able to yield good reconstructed quality.

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Time Domain Image Reconstruction for Homogenous Dielectric Objects by Dynamic Differential Evolution

Cited by 30 publications

References 23 publications

Multiparameter identification of a lossy fluid-like object from its transient acoustic response

Multiparameter identification of a lossy fluid-like object from its transient acoustic response

Computational Approach Based on a Differential Evolution with Self-Adaptive Concept for Microwave Imaging of Two-Dimensional Inverse Scattering Problem

Time Domain Inverse Scattering of Buried Inhomogeneous Dielectric Cylinders

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