Full-Vectorial 3D Microwave Imaging of Sparse Scatterers through a Multi-Task Bayesian Compressive Sensing Approach.

Area of Science:

• Electromagnetics
• Computational Imaging
• Signal Processing

Background:

• Microwave imaging (MI) is crucial for non-invasive subsurface sensing.
• Reconstructing sparse scatterers in 3D poses significant inverse scattering (IS) challenges.
• Existing methods often lack computational efficiency or robustness.

Purpose of the Study:

• To develop an efficient and reliable method for full-vectorial 3D microwave imaging of sparse scatterers.
• To formulate the 3D inverse scattering problem within the contrast source inversion (CSI) framework.
• To retrieve the sparsest and most probable contrast source distribution.

Main Methods:

• Formulation of the 3D inverse scattering problem using the contrast source inversion (CSI) framework.
• Application of a customized multi-task Bayesian compressive sensing (MT-BCS) method.
• Development of regularized solutions for efficient 3D-IS problem solving.

Main Results:

• The proposed MT-BCS method demonstrates remarkable computational efficiency.
• Numerical results validate the effectiveness and reliability of the MT-BCS strategy.
• Performance is competitive with state-of-the-art approaches on benchmark datasets.

Conclusions:

• The MT-BCS approach provides an effective solution for 3D microwave imaging of sparse scatterers.
• The method offers a computationally efficient and reliable alternative for inverse scattering problems.
• This work advances the capabilities of 3D microwave imaging for complex scenarios.