Salucci, M. and Poli, L. and Anselmi, N. and Massa, A. (2017) An Evolutionary Optimization Method for Solving the Multi-Frequency GPR Subsurface Problem. Technical Report. ELEDIA Research Center - University of Trento.
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Abstract
In this work, the two-dimensional subsurface imaging problem is solved by processing wide-band ground penetrating radar (GPR) data. Towards this end, an innovative microwave inverse scattering technique is presented. The developed methodology is based on a particle swarm optimization (PSO) solver to minimize the arising multi-frequency (MF) cost function measuring the mismatch between measured and retrieved data. Moreover, an iterative multi-resolution strategy is exploited in order to progressively and adaptively refine the resolution of the retrieved images only within the regions of interest in which the presence of a buried object has been detected. Some numerical results are shown, in order to assess the effectiveness of the developed MF-IMSA-PSO strategy in reconstructing dielectric objects buried at different depths. Some experiments are also shown to verify the robustness of the proposed method when the background permittivity is not constant but smoothly varying with the distance from the interface.
| Item Type: | Monograph (Technical Report) |
|---|---|
| Uncontrolled Keywords: | Ground Penetrating Radar (GPR), Inverse Scattering (IS), Multi-Frequency (MF), Particle Swarm Optimization (PSO), Stochastic Optimization, Wide-band Data, Iterative Multi Scaling Approach (IMSA) |
| Subjects: | A Areas > A WC Next Generation Wireless Communications M Methodologies > M EA Evolutionary Algorithms |
| URI: | http://www.eledia.org/students-reports/id/eprint/740 |
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