On the exploitation of the a-priori information through the Bayesian compressive sensing for microwave imaging

2012 ◽  
Author(s):  
Giacomo Oliveri ◽  
Lorenzo Poli ◽  
Andrea Massa
Keyword(s):  
Compressive Sensing ◽  
A Priori ◽  
Microwave Imaging ◽  
A Priori Information ◽  
Priori Information

scholarly journals A Synthetic Aperture Radar (SAR)-Based Technique for Microwave Imaging and Material Characterization

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 373 ◽  
Author(s):  
Yuri Álvarez López ◽  
María García Fernández ◽  
Raphael Grau ◽  
Fernando Las-Heras
Keyword(s):  
Synthetic Aperture Radar ◽  
Material Characterization ◽  
A Priori ◽  
Microwave Imaging ◽  
Synthetic Aperture ◽  
Sar Image ◽  
A Priori Information ◽  
Priori Information ◽  
Constitutive Parameters ◽  
Aperture Radar

This contribution presents a simple and fast Synthetic Aperture Radar (SAR)-based technique for microwave imaging and material characterization from microwave measurements acquired in tomographic systems. SAR backpropagation is one of the simplest and fastest techniques for microwave imaging. However, in the case of heterogeneous objects and media, a priori information about the constitutive parameters (conductivity, permittivity) is needed for an accurate imaging. In some cases, a first guess of the constitutive parameters can be extracted from an uncorrected SAR image, and then the estimated parameters can be introduced in a second step to correct the SAR image. The main advantage of this methodology is that there is little or no need for a priori information about the object to be imaged. Besides, calculation time is not significantly increased with respect to conventional SAR, thus allowing real-time imaging capabilities. The methodology has been validated by means of measurements acquired in a cylindrical setup.

scholarly journals Multi-Frequency GPR Microwave Imaging of Sparse Targets through a Multi-Task Bayesian Compressive Sensing Approach

2021 ◽  
Vol 7 (11) ◽  
pp. 247
Author(s):  
Marco Salucci ◽  
Nicola Anselmi
Keyword(s):  
Compressive Sensing ◽  
Quantitative Imaging ◽  
A Priori ◽  
Wide Band ◽  
Frequency Diversity ◽  
A Priori Information ◽  
Spectral Components ◽  
Priori Information ◽  
The One ◽  
Ground Penetrating

An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data by jointly processing the multi-frequency (MF) spectral components of the collected radargrams. On the other hand, it enforces sparsity priors on the problem unknowns to yield regularized solutions of the fully non-linear scattering equations. Towards this end, a multi-task Bayesian compressive sensing (MT-BCS) methodology is adopted and suitably customized to take full advantage of the available frequency diversity and of the a-priori information on the class of imaged targets. Representative results are reported to assess the proposed MF-MT-BCS strategy also in comparison with competitive state-of-the-art alternatives.

Sign in / Sign up

Export Citation Format

Share Document