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.
Newton-Kantorovitch algorithm applied to an electromagnetic inverse problem
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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.