Anselmi, N. and Oliveri, G. and Hannan, M. and Salucci, M. and Massa, A. (2017) Innovative Alphabet‐Based Bayesian Compressive Sensing Technique for Imaging Targets with Arbitrary Shape. Technical Report. ELEDIA Research Center - University of Trento.
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Abstract
In this work an innovative two-dimensional (2D) microwave imaging technique exploiting Bayesian Compressive Sensing (BCS) and a wavelet-based alphabet for representing the problem unknowns is dealt with. The proposed approach is based on the generalization of the sparsity concept, extending the range of applicability of BCS-based inverse scattering (IS) techniques to objects with arbitrary shape and dimensions. A set of BCS reconstructions is performed considering different expansion bases in the alphabet, without the need for a-priori knowledge about the unknown scatterers. Then, the best reconstruction is recognized as that minimizing the number of non-null retrieved coefficients (i.e., the sparsest one). In order to verify the effectiveness of the proposed imaging technique, a set of representative numerical benchmarks is presented. Some comparisons with state-of-the-art IS techniques are presented, as well.
| Item Type: | Monograph (Technical Report) |
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| Uncontrolled Keywords: | Inverse Scattering (IS), Bayesian Compressive Sensing (BCS), Microwave Imaging, First Order Born Approximation |
| Subjects: | A Areas > A WC Next Generation Wireless Communications M Methodologies > M CS Compressive Sensing |
| URI: | http://www.eledia.org/students-reports/id/eprint/742 |
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