A linearized cascade of two linear functionals combined with sum-of-l1-norm optimization enables efficient 3D EM contrast source inversion for half-space problems by computing total fields only once.
Theoretical and empirical results for recovery from multiple measurements
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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.
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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Linearized 3-D Electromagnetic Contrast Source Inversion and Its Applications to Half-space Configurations
A linearized cascade of two linear functionals combined with sum-of-l1-norm optimization enables efficient 3D EM contrast source inversion for half-space problems by computing total fields only once.
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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.