Imaging sparse scatterers through a multi-frequency CS approach

In this paper an inverse scattering technique based on the multi-task Bayesian Compressive Sensing is presented within a multi-frequency framework. After recasting the problem in a probabilistic sense, the solution to the imaging problem is determined by means of an efficient Relevant Vector Machine coupled with a contrast source inversion procedure. Selected numerical results are discussed to assess and compare the efficiency and robustness of the proposed strategy with respect to the state-of-the-art techniques.

[1]  Francesco Soldovieri,et al.  Combination of Advanced Inversion Techniques for an Accurate Target Localization via GPR for Demining Applications , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[2]  A. Abubakar,et al.  Microwave Biomedical Data Inversion Using the Finite-Difference Contrast Source Inversion Method , 2009, IEEE Transactions on Antennas and Propagation.

[3]  David B. Dunson,et al.  Multitask Compressive Sensing , 2009, IEEE Transactions on Signal Processing.

[4]  Andrea Massa,et al.  Multiresolution subspace-based optimization method for inverse scattering problems. , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  Stavros M. Panas,et al.  Microwave imaging using the finite-element method and a sensitivity analysis approach , 1999, IEEE Transactions on Medical Imaging.

[6]  A Massa,et al.  Bayesian compressive optical imaging within the Rytov approximation. , 2012, Optics letters.

[7]  Paolo Rocca,et al.  A Bayesian-Compressive-Sampling-Based Inversion for Imaging Sparse Scatterers , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Mario Bertero,et al.  Introduction to Inverse Problems in Imaging , 1998 .

[9]  Federico Viani,et al.  MT – BCS-Based Microwave Imaging Approach Through Minimum-Norm Current Expansion , 2013, IEEE Transactions on Antennas and Propagation.

[10]  Lin Li,et al.  Buried Object Characterization Using Ultra-Wideband Ground Penetrating Radar , 2012, IEEE Transactions on Microwave Theory and Techniques.

[11]  T. Isernia,et al.  Improved Sampling Methods for Shape Reconstruction of 3-D Buried Targets , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Paolo Rocca,et al.  Bayesian Compressive Sensing Approaches for the Reconstruction of Two-Dimensional Sparse Scatterers Under TE Illuminations , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[13]  T. Isernia,et al.  The Linear Sampling Method as a Way to Quantitative Inverse Scattering , 2012, IEEE Transactions on Antennas and Propagation.

[14]  A. Massa,et al.  A Nested Multi-Scaling Inexact-Newton Iterative Approach for Microwave Imaging , 2012, IEEE Transactions on Antennas and Propagation.

[15]  Michael E. Tipping Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[16]  Andrea Boni,et al.  An innovative real-time technique for buried object detection , 2003, IEEE Trans. Geosci. Remote. Sens..

[17]  J. Richmond Scattering by a dielectric cylinder of arbitrary cross section shape , 1965 .

[18]  Andrea Massa,et al.  Imaging sparse metallic cylinders through a local shape function Bayesian compressive sensing approach. , 2013, Journal of the Optical Society of America. A, Optics, image science, and vision.

[19]  Manuel Benedetti,et al.  Multiple-Shape Reconstruction by Means of Multiregion Level Sets , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Lawrence Carin,et al.  Bayesian Compressive Sensing , 2008, IEEE Transactions on Signal Processing.

[21]  Andrea Massa,et al.  Reconstruction of two-dimensional buried objects by a differential evolution method , 2004 .

[22]  Federico Viani,et al.  Sparse scatterers imaging through approximated multitask compressive sensing strategies , 2013 .

[23]  A. Massa,et al.  Microwave Imaging Within the First-Order Born Approximation by Means of the Contrast-Field Bayesian Compressive Sensing , 2012, IEEE Transactions on Antennas and Propagation.

[24]  Eric L. Miller,et al.  Subsurface Sensing of Buried Objects Under a Randomly Rough Surface Using Scattered Electromagnetic Field Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Magda El-Shenawee,et al.  Efficient Microwave Imaging Algorithm Based on Hybridization of the Linear Sampling and Level Set Methods , 2013, IEEE Transactions on Antennas and Propagation.

[26]  L. Blanc-Féraud,et al.  A new regularization scheme for inverse scattering , 1997 .

[27]  G. Franceschetti,et al.  On the degrees of freedom of scattered fields , 1989 .