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Robust Multi-Frequency Subsurface Imaging through Evolutionary Optimization
In this work, an innovative stochastic method for subsurface microwave imaging is presented. The proposed approach solves the subsurface inverse scattering problem by jointly processing multiple frequency components of the measured wide-band ground penetrating radar (GPR) data. Moreover, an iterative multi-zooming approach is adopted, in order to reduce the ratio between problem unknowns and informative data, as well as to adaptively enforce increased resolutions in correspondence with the identified regions of interest. The minimization of the multi-frequency (MF) cost function is performed at each multi-resolution step by means of a customized particle swarm optimization (PSO) algorithm, thanks to its capability of escaping from local minima, corresponding to false solutions of the inverse scattering problem. Some numerical results are shown, in order to assess the performance of the developed MF-IMSA-PSO method in retrieving buried targets having different shape and composition, as well as to compare it to a deterministic implementation within the same framework (i.e., the MF-IMSA-CG).
Robust Multi-Frequency Subsurface Imaging through Evolutionary Optimization (Text)
HTML Summary of #734 Robust Multi-Frequency Subsurface Imaging through Evolutionary Optimization
A WC Next Generation Wireless Communications
M EA Evolutionary Algorithms