A linear MMV-based inverse scattering model with joint sparsity regularization is introduced for single-frequency imaging of highly conductive objects, showing higher resolution than linear sampling methods on synthetic and Fresnel data.
Electromagnetic inverse scattering of multiple perfectly conducting cylinders by differential evolution strategy with individuals in groups (GDES)
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A GMMV-based iterative linear method with cross-validation for TM electromagnetic shape reconstruction shows better focusing than the linear sampling method on experimental data.
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A Linear Model for Microwave Imaging of Highly Conductive Scatterers
A linear MMV-based inverse scattering model with joint sparsity regularization is introduced for single-frequency imaging of highly conductive objects, showing higher resolution than linear sampling methods on synthetic and Fresnel data.
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A Linear Method for Shape Reconstruction based on the Generalized Multiple Measurement Vectors Model
A GMMV-based iterative linear method with cross-validation for TM electromagnetic shape reconstruction shows better focusing than the linear sampling method on experimental data.